AN ABSTRACT OF THE THESIS OF
Ying Zhang for the degree of Master of Science in Food Science and Technology presented on February 17, 2015.
Title: Investigations of Gluten Proteins from Functional and Historical Perspectives
Abstract approved: ______Andrew S. Ross
Wheat (Triticum aestivum) is one of the world’s major staple food crops, with flour produced from starchy endosperm being used for breads, cakes, noodles and various other wheat-based foods. The unique bread making properties of wheat are primarily attributed to its gluten-forming storage proteins: gliadins and glutenins. This study investigated the gluten proteins from functional and historical perspectives.
The first study examined primarily the functional role of gluten proteins in the outcomes of the standard Falling Number (FN) test. The FN test is used in the grain trade to screen delivered wheat for the presence of pre-harvest sprouting by indirectly measuring α- amylase through it effects on the physical consistency of a cooked flour-water suspension.
Grain protein content (GPC) has been implicated as a potential modifier of FN independent of α-amylase or sprout status. In the gluten functionality study, we proposed a protein unfolding and crosslinking model, and hypothesized that gluten proteins with higher molecular weight distributions (MWD) would heatset faster, tightly cover starch granules, restrict water entry, and slow their disintegration. In contrast to our hypothesis,
our results showed that samples with lower MWD had faster heatset times than samples with higher MWD according to a controlled heating test. We also hypothesize that increased granularity of hard wheat flour reduces the surface area to volume ratio so the starch granules embedded in the particles need more time to hydrate or swell. However, our results indicated that natural variations in flour particle size from a standard grinding procedure that used a 0.8 mm screen had no impact on FN.
The second study looked at potential changes in gluten proteins in a historical set of wheat varieties spanning more than 110 years of production. The wheats were in two sets: soft wheats where there has been no systematic selection for increased dough strength in breeding programs, and hard wheats where there has been a concerted effort to increase overall dough strength over the last century. The sample sets also covered the eras before and after the introduction of the semi-dwarf wheats to the USA. The reason for this investigation is related to the circumstance that wheat is the cause of celiac disease (CD) and is implcated in the disputed condition, non-celiac gluten sensitivity (NCGS).
Recently, diagnoses of CD at least have increased and there are suggestions that changes in gluten proteins in the modern era are responsible. Since it is primarily gliadins that trigger CD, it was considered worth investigating whether or not there have been any changes in the composition of gliadins over the last century. Sixty-two soft and 61 hard
U.S. high production wheat varieties from 1900 to the present (with one from 1800) were collected and analyzed by RP-HPLC. These varieties were investigated to begin to answer whether wheat breeding for higher dough strength, or the incorporation of dwarfing alleles after the 1960s, was associated with observable changes in gliadin
composition. ANOVA showed that there was no significant difference between soft and hard wheats in the relative abundance of α/β-gliadins. However, there were significant differences between hard and soft wheats in the relative abundance of ω- and γ-gliadins.
ANOVA also showed that there was no significant difference between tall and dwarf wheats in the relative abundance of any of the three gliadin fractions. The ANOVA results suggested that deliberate breeding for dough strength, as illustrated by the hard versus soft wheat contrast, had not systematically changed the relative abundance of α/β- gliadins across the last 110 years, but had altered the relative abundance of the other two fractions. ANOVA results indicated no change in proportions of the three gliadin fractions after deployment of the dwarfing alleles suggesting the tall to dwarf change was independent of gluten composition. Second order polynomial regression analyses showed that the relative abundance of α/β-gliadins increased until around 1960 then decreased.
The changes were more noticeable in the hard wheats. The converse was observed for 훾- gliadins. This stepwise change questioned the association between CD increase and breeding for increased dough strength in hard wheats, since the relative abundance of
α/β-gliadins did not keep going up, and α-gliadin is considered the major trigger force for
CD initiation. In contrast, linear correlation analyses with each of 700, three second long fractions of the RP-HPLC chromatograms suggested that most changes were related to the soft wheat population. The discrepancy between the regression analyses of the three major fractions and the 700 small fractions may be related to the use of linear correlations in the latter when some relationships were clearly non-linear. Overall, our results did not fully support speculations that there have been profound changes in gluten composition related to the dwarfing alleles or selection for increased dough strength in hard wheats.
©Copyright by Ying Zhang February 17, 2015 All Rights Reserved
Investigations of Gluten Proteins from Functional and Historical Perspectives
by
Ying Zhang
A THESIS
submitted to
Oregon State University
In partial fulfillment of the requirements for the degree of
Master of Science
Presented February 17, 2015 Commencement June 2015
Master of Science thesis of Ying Zhang presented on February 17, 2015
APPROVED:
Major Professor, representing Food Science and Technology
Head of the Department of Food Science and Technology
Dean of the Graduate School
I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request.
Ying Zhang, Author
ACKNOWLEDGEMENTS
I would like to express my appreciation to all the people who contributed to the successful completion of this thesis. My advisor, Dr. Andrew Ross, deserves a very special acknowledgement for his patient guidance and assistance during this research. He gave me the opportunity to continue working on my interest area – instrumental analysis of biomolecules. I would like to thank Drs. Teepakorn Kongraksawech, Michael Penner, and Joseph McGuire for serving on my committee.
I would also like to thank Colleen Roseborough for her help in materials supply and Dr.
Jae Ohm for his time in data analysis.
My sincere thanks to Caryn Ong, Dr. Teepakorn Kongraksawech, Mike Adams, Carlos
Fajardo, Eva Kuhn, Joshua Evans, Omar Miranda-Garcia, Michayla Robertson and
Jordan Smith in the Ross Lab. They provided tremendous help on this work. It was always a great time working with them and I am sure I will miss that.
I would like to express my deepest gratitude towards my husband Kain Wu for his love, encouragement and unconditional support on every decision I made. Finally, I would like to thank my parents, whose infinite love and support made it possible for me to complete my study in OSU.
CONTRIBUTION OF AUTHORS
Dr. Andrew Ross and Dr. Robert Zemetra initiated the project. Dr. Andrew Ross guided the project and contributed to the manuscripts. Dr. Teepakorn Kongraksawech assisted with laboratory procedures. Dr. Jae Ohm performed the MATLAB interpolations of the
RP-HPLC chromatogram data and assisted in their statistical interpretation. Colleen
Roseborough conducted materials preparation for the historical study.
TABLE OF CONTENTS
Page
Chapter 1: General Introduction..……………………………………………………1
1.1 Background……………………………………………………………….….1
1.2 Objectives and hypotheses…………….……………………………………..5
Chapter 2: Literature Review………………………………………………………...7
2.1 Wheat..…………………………………………………………….…………7
2.2 Wheat types and general uses.………….……………………………………8
2.3 Wheat kernel anatomy and composition…….………………………………10
2.4 Kernel physical texture………………………………………………………11
2.5 Wheat flour milling.…………………………………………………………13
2.6 Wheat proteins………………………………………………………………15
2.6.1 Gliadins………………………………………………………………….16
2.6.2 Glutenins...………………………………………………………………19
2.7 Wheat carbohydrates…….…………………………………………………..22
2.7.1 Starch...……………………………………………………………………22
2.7.2 Non-starch polysaccharides...……………………………………………28
2.8 Wheat lipids…….…………………………………………………...……….33
2.9 Wheat enzymes…….…………………………………………………...……34
2.9.1 Starch-degrading enzymes.………………………………………………35
2.9.2 Protein-degrading enzymes....……………………………………………36
2.9.3 Oxidoreductases………………………………………………………….37
2.10 The flour-water system during processing.…………………………………38
TABLE OF CONTENTS (Continued)
Page
2.11 Gluten intolerance..…………………………………………………………42
References.………….……………………………………………………………44
Chapter 3: Investigations of Non-amylase Factors affecting Wheat Falling Number Results..…………………………………………………………45
Abstract………………..…………………………………………………………45
3.1 Introduction...………….………………………………..……………………45
3.2 Materials and methods..………………………………………………………49
3.2.1 Materials....…………….…………………………………………………49
3.2.2 Grain and flour physicochemical properties.…….……………………….51
3.2.3 Polymeric proteins analysis.…………….………………………………..52
3.2.4 Mixograph method...…….………………………………………….……56
3.2.5 Pasting properties..….……………………………………………………56
3.2.6 Particle size analysis...……………………………………………………58
3.2.7 Statistical analyses...….……………………………….………………….59
3.3 Results and Discussion..………….………………………………..…………59
3.3.1 New SE-HPLC column validation………………………………………..59
3.3.2 Thermal stability of gluten proteins in a heated flour-water system...……63
3.3.3 Relationship between flour particle size and falling number……………..71
3.4 Conclusions…………….………………………………..……………………74
References..………….……………………………………………………………75
Chapter 4: Investigation of Gliadin Proteins in Wheat Varieties Dating From 1800 to 2011.………………………………………………………………78
TABLE OF CONTENTS (Continued)
Page
Abstract………………..………………………………………………………….78
4.1 Introduction…………….………………………………..……………………79
4.2 Materials and methods..………………………………………………………84
4.2.1 Materials.…………….……………………………………………………84
4.2.2 Gliadin analysis...…………………………………………………………86
4.2.3 RP-HPLC chromatogram data analyses…….…………………………….87
4.2.4 Statistical analyses…….……………………………….………………….88
4.3 Results and Discussion...………….………………………………..…………89
4.3.1 Plant height change in decades...………………………………………….89
4.3.2 Effects of kernel hardness and dwarfing alleles on gliadin fractions...……91
4.3.3 Changes in gliadin fractions over time.……………………………………91
4.3.4 Linear correlations between decade of production, and plant height versus % absorbance area of gliadins separated by RP-HPLC.……………94
4.4 Conclusions…………….………………………………..………………….…101
References...……….………………………………………………………………102
Chapter 5: General Conclusion..………………………………………………………106
Bibliography………………………………………………………..………………….108
Appendices……………………………………………………….……………………129
Appendix 1: Samples for flour particle size determination (2011 OWEYT)...……129
Appendix 2: Samples applied in the historical study...……………………………130
LIST OF FIGURES
Figure Page
2.1 Longitudinal and cross sections of a wheat kernel (Dexter and Sarkar, 2004)………………………………………………………….………………10
2.2 Diagram of Brabender Quadramat laboratory mill with different milling fractions (Ross, 2013).………………………………………………………..14
2.3 Physical properties of gluten forming proteins (Delcour and Hoseney, 2010).…………………………………………………………………………16
2.4 (a) Structures of α-/β-, γ- and ω-gliadins and B-, C- and D-low molecular weight (LMW) glutenin subunits, (b) Structures of x- and y-type high molecular weight (HMW) glutenin subunits (Tatham et al., 1990; Shewry et al., 2009) and (c) Structural model of inter-chain disulfide bonds linkage for LMW and HMW glutenin subunits (Wieser et al., 2006..……….18
2.5 (a) Gene loci on the group 1 and 6 chromosomes for main wheat proteins (gliadins and glutenins) (Payne et al., 1987; Singh and Shepherd, 1988) and (b) Allelic variation in HMW glutenin subunits at three gene loci Glu-A1, Glu-B1 and Glu-D1 according to SDS-PAGE results and their relationship to bread quality (Payne et al., 1984).……………………………18
2.6 Molecular structure of starch polymer: (a) amylose and (b) amylopectin (Pérez et al., 2009).…………………………………………………………...24
2.7 Semicrystalline structure of starch granule (Donald et al.,1997; Buléon et al., 1998)...………………………………………………………………….25
2.8 Chemical structure of arabinoxylans (AX). (a) Unsubstituted D-xylopyranosyl residue, (b) monosubstituted AX at O-3 with a ferulic acid moiety on the arabinose molecule, (c) monosubstituted AX at O-2 with α-L-arabinofuranosyl residue and (d) disubstituted AX at both O-2 and O-3 (Izydorczyk and Dexter, 2008)………………………………………30
2.9 Comparison of stirring number curves with standard RVA curves according to three different FN wheatmeals. Temperature profiles are at the top (Bason and Blakeney, 2009)..………………………………………36
2.10 Loop and train model to explain glutenin elasticity. (1) Equilibrium state, (2) Small extension and (3) Large extension (Belton, 1999).……….….40
LIST OF FIGURES (Continued)
Figure Page
3.1 Representative SE-HPLC chromatograms of old (a1 and b1) and new (a2 and b2) columns (sample: Norwest 553, Condon, 2011). (a) First extraction: SDS-extractable proteins and (b) Second extraction SDS- unextractable proteins. Both chromatograms were separated into four parts including LPP, SPP, LMP and SMP. A and A’ represent the sum of LPP and SPP fractions, B and B’ represent the LMP fraction as well as C and C’ represent the SMP fraction....……………………………………55
3.2 Correlation analysis between old and new column %LUPP responses based on the same sample set....………………………………………………61
3.3 Typical good (a) and poor (b) quality mixograph results.…………………….62
3.4 Correlation analyses between Mixograph peak time in minute versus new (a) and old (b) column %LUPP………………………………………….62
3.5 Results of fasting heating experiments. (a) Same GPC in both hard and soft wheat varieties (GPC = 11.8% in Norwest 553 & GPC = 11.7% in ORSS-1757), (b) different GPC in hard wheat variety Norwest 553 (GPC = 14.0% & 9.6%) and (c) different GPC in soft wheat variety ORSS-1757 (GPC = 11.7% & 8.4%). Data collected at 0 s was control 2...….……………66
3.6 Results of controlled heating experiments. (a) Same GPC in both hard and soft wheat varieties (GPC = 11.8% in Norwest 553 & GPC = 11.7% in ORSS-1757), (b) different GPC in hard wheat variety Norwest 553 (GPC = 14.0% & 9.6%) and (c) different GPC in soft wheat variety ORSS -1757 (GPC = 11.7% & 8.4%). Data collected at 20 ℃ was control 2.………70
3.7 Correlation analysis between mean FN (sec) and kernel hardness (results obtained from SKCS)…………………………………………………………72
4.1 Representative RP-HPLC chromatogram of gliadins. Three HPLC fractions collected at 10-25, 25-33 and 33-45 min were α/β-, ω- and γ-gliadins respectively...………………………………………………………87
4.2 Linear and polynomial regressions of proportional abundance different gliadin fractions across decades for the entire dataset, and for soft wheats and hard wheats viewed separately……………………………………………93
LIST OF FIGURES (Continued)
Figure Page
4.3 Plots (correlograms) of linear correlation coefficients (r) for decades of peak production versus %AA of protein extracts for each 0.05 min time interval as separated by RP-HPLC for the entire dataset (A), soft wheats (B) and hard wheats (C). A representative chromatogram is shown as for reference purposes..……………………………………………98
4.4 Plots (correlograms) of linear correlation coefficients (r) for plant height versus %AA of protein extracts for each 0.05 min time interval as separated by RP-HPLC for the entire dataset (A), soft wheats (B) and hard wheats (C). A representative chromatogram is shown as for reference purposes...…………………………………………………………99
LIST OF TABLES
Table Page
2.1 Distribution of crude fats in whole-wheat grain (Morrison, 1978).…………33
3.1 Grain protein content, falling number and hardness of selected samples (Same GPC and same LUPP)..………………………………………………64
3.2 Two-way ANOVA results of control effect…………………………………64
3.3 Onset temperatures under controlled heating..………………………………69
3.4 Correlations between mean FN and wheat particle size of three selected varieties....……………………………………………………………………73
3.5 Correlations between mean FN and wheat particle size of 50 randomly selected samples...……………………………………………………………73
4.1 Average plant height (cm) by decade..………………………………………89
4.2 Analyses of Variance for the effects of soft versus hard and tall versus dwarf…………………………………………………………………………90
4.3 Mean and Standard Error for proportional abundance of different gliadin fractions for soft versus hard and tall versus dwarf wheats.…………………90
4.4 Fractions of significant correlation (report as retention time fractions in minute) for decade of peak production, and plant height versus %AA of protein extracts for each 0.05 min time interval as separated by RP-HPLC in the entire dataset, soft wheats and hard wheats correlograms……………100
DEDICATION
This thesis is dedicated to my beautiful daughter Reine Wu.
1
Chapter 1: General Introduction
1.1 Background
Wheat is one of the world’s major staple food crops, with flour produced from starchy endosperm being used for breads, cakes, noodles and various other wheat-based foods
(Atwell, 2001a). In 2012, over 670 million metric tons of wheat were produced worldwide, about 9% (62 million metric tons) was produced in the United States (USDA,
2013). Most of commercial wheat cultivars used for food are from the hexaploid Triticum aestivum species (Wrigley, 2009). Wheat grain has high protein content and plays an important role in vegetable protein supply (Ciccoritti, 2013). The unique bread making properties of wheat are primarily attributed to its gluten-forming storage proteins
(Delcour et al., 2012). This dissertation is focused on gluten proteins from both functional and historical perspectives.
Gluten proteins
Gluten proteins, composed of gliadins and glutenins, vary in molecular weight, size and rheological properties. Gliadins are a heterogeneous mixture of monomeric proteins consisting of α, β, γ and ω types. Ω-gliadins lack cysteine residues and therefore no inter- or intra-molecular disulfide bond formation occurs in ω-gliadins (Hsia and Anderson,
2013). Cysteine residues in α-/β- and γ-gliadins are located at highly conserved positions within the peptide structure. As a result, they do not participate in polymerization reactions mediated through inter-molecular disulfide bonds. Instead, they only possess intra-molecular disulfide bonds and appear folded in conformation under ambient 2 conditions (Delcour et al., 2012; Bonomi et al., 2013). In contrast, polymeric glutenins are a group of macromolecules consisting of different glutenin subunits, including high molecular weight glutenin subunits and low molecular weight glutenin subunits (Southan and MacRitchie, 1999). Both intra- and inter-molecular disulfide bonds exist within and between glutenins. The cysteine residues that engage in intra- or inter-chain disulfide bond formation are located on the ends of the glutenin subunits (Delcour et al., 2012).
Upon hydration and mechanical mixing glutenins and gliadins aggregate to form the viscoelastic mass known as gluten. Hydrated gliadins are sticky and contribute to dough’s viscous (flow) characteristics. Glutenins contribute dough elasticity. This unique cohesive and viscoelastic behavior explains why wheat flour has the ability to form strong cohesive gas-trapping films in dough when mixed with water (Delcour and
Hoseney, 2010c).
Heating breaks hydrogen bonds in wet gluten and increases network mobility, facilitating cross-linking between different protein fractions (Guerrieri et al., 1996; Lefebvre et al.,
2000). Under such thermal treatment, sulfhydryl (SH) – disulfide (SS) interchanges happen in both glutenins and gliadins. Continued heating results in protein polymerization, in which covalent disulfide linkage is the key factor (Lagrain et al., 2005).
The oxidation of SH groups causes the polymerization of glutenin subunits, as well as the aggregation between gliadin and glutenin molecules (Weegels et al., 1994; Singh and
MacRitchie, 2004; Lagrain et al., 2008).
3
Pre-harvest sprouting determined by Falling Number
Wheat quality is strongly affected by pre-harvest sprouting (PHS). PHS is an undesirable phenomenon and primarily results from rain falling on harvest mature grain. Upon germination, large amounts of α-amylase (a starch-degrading enzyme) are synthesized
(Mansour, 1993). However, the activity of α-amylase needs to be maintained at an optimal level during bread manufacturing and as a result, in practice, millers prefer to source sound, unsprouted grain and dose α-amylase at controlled levels. This is because the presence of excess α-amylase can have a negative impact on both processing and on end-product quality: e.g. sticky doughs and poor crumb textures (Goesaert et al., 2006).
PHS can be evaluated by a number of methods and Falling Number (FN) is by far the most common method used to monitor PHS in grain trading. Basically, FN measures the physical consistency of a flour paste by measuring the time in seconds for a free-falling plunger to descend a prescribed distance (AACCI Approved Method 56-81.03, 2010).
Sprout-damaged samples are expected to have lower FNs than undamaged ones due to less resistance to the falling plunger. Normally high FN (> 300 s) indicates sound
(unsprouted) wheat and low FN (< 300 sec) is considered evidence of the presence of excess α-amylase as a result of sprouting (Perten, 1964; McFall and Fowler, 2009).
However, wheat with low FN in the absence of elevated AA has been recently reported
(Ross et al., 2012). Upon testing soft white winter wheat samples from the 2011 harvest, they found that grain protein content (GPC) and grain hardness could be two other important factors modulating FN results independent of α-amylase activity (i.e. non- 4 amylase factors). Demonstration of this correlation helped establish a new baseline for
FN testing, since sound wheat produced in environments that tend to produce low GPC grain may also produce low FN grain that may wrongly be rejected for PHS under current standards.
Celiac disease
Wheat has been selected for desirable traits for centuries. E.g., hard wheats, used for dough-based products like breads, have undergone selection for higher dough strength.
Another commonly selected trait is plant height. Genetically “wild-type” wheat (Rht-B1a and Rht-D1a) is a tall plant (> 120 cm). Long stalks easily bend from the weight of the spike during grain filling. Subsequently, lodging and yield loss can occur. A significant change occurred after 1960 when O.A. Vogel incorporated the dwarfing alleles (Rht-B1b and Rht-D1b) into U.S. wheats and created high yielding wheat cultivars < 100 cm tall
(Ram, 2014).
Wheat has been implicated, with or without evidence, in some human pathologies. The most recognized is celiac disease (CD), an immune-mediated gut disorder initiated by the ingestion of gluten (Wieser and Koehler, 2008; Shewry, 2009). Symptoms include abdominal pain, anemia, vomiting, diarrhea, etc. A unique region of α-gliadin triggers
CD, with an emphasis on a 33-mer immunogenic peptide (Shan et al., 2002). Another gluten-associated disorder has been suggested where patients have negative CD test results, exhibit no duodenal mucosa damage, but share some, but less severe, CD symptoms. This disorder is termed “non-celiac gluten sensitivity” (NCGS). There is 5 debate about whether “NCGS” is associated with gluten or other wheat components
(Biesiekierski et al., 2013).
Evidence suggests that the incidence of CD has increased (Rubio-Tapas et al. 2009). Van den Broeck et al. (2010) suggest this increase came from wheat breeding that inadvertently or otherwise presented a greater frequency of CD epitopes (e.g. Gliadin-α9) as a side effect of breeding for increased dough strength. William Davis (2011) in the sensationalist book “Wheat Belly” speculated that the introduction of dwarfing alleles caused a change in wheat gluten proteins that negatively affected human health.
Therefore, how gluten composition has, or has not, changed through breeding across time is the main focus of the historical study.
1.2 Objectives and hypotheses
Chapter 2
Chapter 2, the literature review, provides a detailed description on different wheat types, kernel anatomy, kernel physical texture, flour milling, and chemical composition (protein, starch, non-starch polysaccharides, lipids, enzymes). It also introduces the elementary chemical and physical changes of wheat flour in an excess water system under thermal treatment as well as reviews recent perspectives related to celiac disease.
Chapter 3
In this gluten functionality study, underlying mechanisms of biopolymer behavior in a flour-water system during hydrothermal processing were investigated. We propose a 6 protein unfolding and crosslinking model, and hypothesize that gluten proteins with higher molecular weight distributions will heatset faster, tightly cover starch granules, restrict water entry, and slow their disintegration. Since milling harder grains usually results in higher mean particle size in the resultant flour, we also hypothesize that increased granularity of the hard wheat flour reduces the surface area to volume ratio so the starch granules embedded in the particles need more time to hydrate or swell. In either situation, by inference the starch granules also take longer to breakdown during FN measurement and a larger FN will be obtained.
Chapter 4
In this historical study, we applied an alternative approach to investigate if there were any potential changes across time in the composition of gluten. We wanted to begin to address whether breeding for higher dough strength or reduced plant height may have been associated with increased CD prevalence. Our hypothesis is that if gluten has changed systematically over time, and with the introduction of the dwarfing alleles, we should be able to observe systematic or stepwise changes in gliadins in high production wheat varieties grown over the last 110 years.
7
Chapter 2: Literature Review
2.1 Wheat
Wheat is one of the world’s major staple food crops. Several attributes of both the wheat plant and its seeds make wheat a basic staple food. First, wheat plants can grow under a wide range of environmental and soil conditions making worldwide cultivation possible.
Second, a wide variety of popular food products such as bread, cake, cookie, noodle, etc. are based on the flour obtained from the wheat seeds. Also, the nutritional value of wheat is relatively high. For example: Wheat grain has higher protein content than the other cereal crops and plays an important role in vegetable protein supply (Ciccoritti, 2013).
Wheat is in the genus Triticum. Ancient cultivated wheat was T. monococcum (einkorn), a diploid with the Triticum A genome (AA: 2n = 14, contains seven chromosome pairs).
Diploids Aegilops speltoides and T. tauschii were also ancestors of polyploid wheat species, and were the sources of B and D genomes respectively (Stallknecht et al., 1996;
Wrigley, 2009). Later, the cultivated tetraploid emmer T. dicoccum and durum T. durum evolved with genome AABB (2n = 28), resulting from the natural hybridization of T. monococcum and A. speltoides. So called “modern” common wheat T. aestivum is hexaploid with genome AABBDD (2n = 42). This species originated from a natural hybridization event between wild emmer T. dicoccoides and T. tauschii (Kimber and
Sears, 1987). Currently, most of the commercial wheat cultivars used for food are hexaploid T. aestivum and tetraploid T. durum species, each used for different but overlapping food applications (Wrigley, 2009). 8
2.2 Wheat types and general uses
Kernel hardness, kernel color and growing seasons are three major factors used to classify different common wheat types. In general, hard and soft are used to describe kernel texture (Jolly et al. 1996; Feiz et al. 2008). Hard wheat kernels require more crushing force during the milling than soft wheat kernels. According to the presence or absence of red pigment in the kernel seed coat, wheat can be divided into red and white types (Evers and Bechtel, 1988). Three separate genetic loci control kernel color. Red wheat contains at least one, two, or three active red alleles, resulting in varied reddish levels in red kernels. White wheat only carries recessive red alleles and expresses no pigment in the seed coat (Metzger and Sibaugh, 1970; McIntosh et al. 1998). Based on growth seasons, wheat is further divided into winter and spring types. Winter wheats are planted in autumn and have to spend a period of time under freezing temperatures before they can initiate reproductive growth and form heads containing wheat kernels (Atwell,
2001a; Curtis 2002). This process is called vernalization, which is gene-dependent and associated with VRN1 located on long arms of chromosomes 5A, 5B and 5D (Law et al.
1976; Gooding, 2009). Spring wheat on the other hand does not require a vernalization period. Instead it is planted in spring. Both winter and spring types are harvested in late summer or early autumn (Atwell, 2001a).
Overall, on the basis of the above three classifications, a three letter acronym is used to name commercial wheat types in the USA. For example, hard red winter wheat is designated HRW, etc. Durum is another popular wheat type that differs from common wheat. It has harder kernel with yellowish color that suffuses the endosperm (Morris, 9
2002). In addition, high protein content and low lipoxygenase activity are found in durum
(Aalami et al., 2007). Currently, HRS, HRW, SRW, HW, SW and durum wheat types occupy a large proportion of the market (Atwell, 2001a).
Different wheat types have different optimum usage in end products. Hard wheats including HRW, HW and HRS are generally used in dough-based products such as breads. This is due to gas retaining properties and viscoelastic behaviors of doughs made from flour of these wheat types. Soft wheats (SRW and SW) are generally used in batter- based products such as cakes, and other uses that do not require a developed dough e.g. cookies. The lack of viscoelastic property results from deliberate breeding to have weak dough properties (Pers. Comm. Dr. Ross). Additionally soft wheat gluten proteins may differ fundamentally in their aggregation properties compared to hard wheats (Jazaeri,
2013). Durum wheat is unique and mostly used for pasta products (Atwell, 2001a).
10
2.3 Wheat kernel anatomy and composition
Figure 2.1: Longitudinal and cross sections of a wheat kernel (Dexter and Sarkar, 2004).
The wheat kernel is an egg shaped seed. The average wheat kernel is about 8mm in length and 35mg in weight. The kernel consists of three distinct anatomical parts: bran, endosperm and germ (Figure 2.1). The outer bran is made up of several layers. Pericarp is the outermost tissue that surrounds the entire kernel and it consists of about 5% of the kernel. The adjacent layer is the seed coat, which is fused to the pericarp. The aleurone layer, which belongs anatomically to the endosperm, is next to the seed coat with the nucellar epidermis tightly bound to both of them. Pericarp, seed coat, aleurone and nucellar epidermis form what millers called “bran” and they are separated from the starchy endosperm during milling (Posner and Hibbs, 2005; Delcour and Hoseney, 2010).
Bran is a good source of dietary fiber (primarily insoluble), trace minerals and also contains a small amount of protein (Causgrove et al., 2004).
11
The endosperm makes up about 85% of the wheat grain mass. As described anatomically, but not by flour millers, the outer layer of endosperm is the aleurone. It is a one cell thick layer that covers the entire endosperm. Protein, total phosphorus, phytate phosphorus, lipid, ash contents and enzymatic activities are high compared to the rest of the endosperm. The most abundant component of the endosperm is starch. Starchy endosperm is composed of three kinds of cells (peripheral, prismatic and central) that are present in different size, shape and location within the kernel (Delcour and Hoseney,
2010). White flour originates from endosperm and it is a good source of carbohydrate, protein, iron, vitamin B as well as soluble fiber (Causgrove et al., 2004).
The germ is the embryo of the next generation plant and is 2%-3% weight of the kernel.
It is made up of two major parts, embryonic axis and scutellum. Germ is arguably the most nutritious part of the kernel. It contains protein, lipids, vitamin B, and trace minerals.
Due to the rich lipid content (10%), germ is usually separated from endosperm flour during milling to extend shelf life (Causgrove et al., 2004; Delcour and Hoseney, 2010).
2.4 Kernel physical texture
Kernel physical texture is an important wheat characteristic because it is the fundamental property in the initial determination of the optimal end uses of wheat. The distinction between kernel hardness of hexaploid hard and soft wheats is determined by Hardness
(Ha) locus on the short arm of chromosome 5D. The Ha locus is composed of a pair of closely linked genes coding for the proteins puroindolines a and b (Jolly et al., 1993;
Gautier et al., 1994; Morris et al., 1994). The protein complex friabilin, expressed by Ha 12 locus, is comprised of both puroindoline proteins (Morris, 2002; Feiz et al., 2008).
Mechanistically, kernel hardness is controlled by the degree of adhesion between starch granules and wheat storage protein matrix in the endosperm (Mikulikova, 2007). The presence of friabilin decreases grain hardness by reducing the adhesion between starch granules and the protein matrix, resulting in the ‘soft’ wheat (Anjum and Walker 1991).
Hard wheat is the result of mutation or deletion of genes coding for puroindoline a or b.
For example, the tetraploid durum wheat, which lacks the D genome, has a very hard seed kernel texture (Giroux and Morris, 1998).
The single-kernel characterization system (SKCS) is a widely used method determining wheat kernel texture. It measures four properties of the wheat kernel: hardness, moisture, weight and diameter. Hard and soft wheats can be differentiated based on the force used to crush the kernels. Currently, SKCS method is an indispensable technique in grain quality test (Williams, 2000). Besides SKCS, particle-size index (PSI) is a complementary method of assessing kernel texture. It measures relative kernel hardness by grinding wheat sample and sieving the resultant ground material through a 75-μm sieve. Final PSI% = (The amount passing the 75-μm sieve / Sample weight) × 100.
Generally, lower PSI% indicates harder wheat (AACCI approved method 55-30.01,
2010). Kernel texture is related to the damaged starch level. Increased damaged starch in hard wheats increases the water absorption of their generated flours. In general, hard wheat has more damaged starch than soft wheat (Ross and Bettge, 2009). There are optimal starch damage levels for different end products, breads require more damaged starch while cakes and biscuits require low starch damage level (Miskelly et al., 2010). 13
2.5 Wheat flour milling
Wheat is harvested and sorted by locations or primary traits like protein content before arriving at a mill. Normally the wheat at a mill is a blend of various varieties, harvested from multiple locations and conditions if no identity preservation is required (Atwell,
2001a). In the modern era the vast majority of wheat flour milling is performed with roller mills with the aim to reduce the particle size to a particle size distribution typical of wheat make flour (Code of Federal Regulations 2014, 21CFR137.105, Bass, 1988).
Kernel texture is an important factor that influences milling. Generally, hard wheat requires more energy to mill and results in more damaged starch due to the tight adhesion of starch granule and protein matrix (Anjum and Walker 1991) and the propagation of fractures through starch granules leading to physical damage. However, in soft wheats the loose adhesion of starch granule and protein matrix allows fractures to propagate at the interface between starch granule and protein matrix minimizing physical starch damage
(Bechtel et al., 2009).
Roller milling is a two-step process: tempering and grinding. Laboratory-scale milling is described to highlight the main process steps. Wheat moisture content is determined in advance, often using SKCS, and is typically 12% or below. Water is added for the purpose of tempering. Hard wheat requires more water than soft wheat (15% for hard and
14% for soft). Wheat is tempered overnight prior to milling. Tempering toughens the bran to keep it intact and reduce the chance to be ground into flour particle size (less admixture). It also softens the endosperm to decrease the amount of starch damage. As an example the mill flow of a Brabender Quadramat Senior Laboratory Flour Mill, as used 14 in the OSU cereal quality lab, is described. The mill has two series of three roll passes to separate the bran and germ from endosperm and gradually reduce the particle size of the endosperm (Causgrove et al., 2004). The first set of three roll passes is called the “break” system. Within each pair of roll the rolls counter-rotate (e.g. one clockwise, the other counterclockwise). The gap between rolls decreases with each subsequent roll pass. A laboratory sifter is used to divide the products of the break rolls into three parts: bran, middlings, and break flour. The coarser middlings are milled again in the 2nd set of the roll passes: the “reduction” system. This results in shorts and reduction flour. The combination of break and reduction flours yields ‘straight-grade flour’ (Figure 2.2).
Every milling fraction is weighed in order to evaluate the yields and wheat milling properties (Posner, 2009). The laboratory mill described here is a small-scale and simplified version of mills used in commerce and describes the general principles. Large- scale mills are more complex that focus on the high flour extraction.
Figure 2.2: Diagram of Brabender Quadramat laboratory mill with different milling fractions (Ross, 2013).
15
2.6 Wheat proteins
Currently, wheat is one of the most important sources of vegetable proteins in the human diet. Wheat kernel proteins are classified as nongluten and gluten forming. Nongluten forming proteins correspond to the albumins and globulins (monomeric proteins) that account for about 15% of total wheat proteins (MacRitchie and Lafiandra, 1997; Delcour et al., 2012). Albumins are water soluble and globulins are soluble in a dilute salt solution
(Osborne, 1907; Delcour and Hoseney, 2010). The gluten forming proteins: gliadins and glutenins are barely solublized in water or salt solutions. Gliadins (also known in the terminology of cereal science as “monomeric” proteins) can be extracted using 70% ethanol. The residues are the glutenins (Osborne, 1907; Weegels, et al., 1996; Delcour and Hoseney, 2010). The low water extractability property of gluten proteins results from the amino acid composition primarily glutamine and nonpolar proline (Wieser, 2007).
Due to this difference in solubilities, wheat proteins can be isolated into the four categories (albumins, globulins, gliadins, glutenins) by sequential extraction using appropriate solutions.
Upon hydration and mechanical mixing glutenins and gliadins aggregate to form the viscoelastic mass known as gluten. Gliadins and glutenins vary in molecular weight, size and rheological properties. Hydrated gliadins are sticky and contribute to dough viscosity
(flow), while glutenins appear to be very resilient and mainly account for dough elasticity
(Figure 2.3). This unique cohesive and viscoelastic behavior explains why wheat flour has the ability to form strong cohesive gas-trapping films in dough when mixed with water (Delcour and Hoseney, 2010c). 16
Gluten = Gliadin + Glutenin
Figure 2.3: Physical properties of gluten forming proteins (Delcour and Hoseney, 2010).
2.6.1 Gliadins
Gliadins are monomeric proteins consisting of α, β, γ and ω types (Figure 2.4a). Due to the similar structure (Kasarda et al., 1987), α- and β-Gliadins are usually grouped into one class. They contain three different domains: a short N-terminal domain, a central repetitive domain, and a long C-terminal domain containing six cysteine residues (Bartels et al., 1986). In the SDS-PAGE, γ-gliadins are relatively close to α-/β-gliadins since their molecular weights are both around 30,000Da (Koehler and Wieser, 2013). Three domains can also be found in γ-gliadin structure. However, γ-gliadins have eight cysteine residues in the C-terminal domain. α-/β- and γ-Gliadins are both related to low molecular weight glutenins as expressed through similarity of amino acid sequences (Delcour and Hoseney,
2010). In contrast, ω-gliadins have a peculiar structure and different amino acid composition from α-/β- or γ-gliadins. Ω-gliadin molecular mass is between 40,000Da and
55,000Da (Koehler and Wieser, 2013). Ω-gliadins are characterized by a single repeated domain and a lack of cysteine residues (Hsia and Anderson, 2013). Consequently, there is no disulfide bond formation in ω-gliadins. Cysteine residues in α-/β- and γ-gliadins are 17 located at highly conserved positions within the peptide structure. As a result, they do not participate in polymerization reactions mediated through inter-molecular disulfide bonds
(see below 2.6.2). Instead, they only possess intra-molecular disulfide bonds under ambient conditions (Delcour et al., 2012; Bonomi et al., 2013). Therefore, gliadins appear compact and folded in conformation and they have only weak secondary interactions with other wheat storage proteins.
Syntheses of most wheat storage proteins are encoded on chromosomes 1A, 1B, 1D, 6A,
6B, 6D. These six main loci are collectively known as the gliadin-encoding (Gli) loci
(Figure 2.5a) (Metakovsky et al., 2006). Gli-1 loci (Gli-A1, Gli-B1 and Gli-D1) are located on the short arms of group 1 chromosomes, controlling the synthesis of γ-, ω- and a few β-gliadins. Whereas Gli-2 loci are located on the short arms of group 6 chromosomes and regulate mostly α-/β- and some γ-gliadin synthesis. Additionally, some
ω-gliadins are also controlled by Gli-A3 and Gli-B3 minor loci on the long arms of group
1 chromosomes (Gianibelli et al., 2001).
Reversed-phase high-performance liquid chromatography (RP-HPLC) has been shown to be a powerful and efficient tool in quantitative characterization of gliadin types in wheat flour (Wieser, 1998). Different gliadins are eluted based on their distinct surface hydrophobicities. Ω-gliadins elute first, followed by α-/β- and γ-gliadins (Wieser, 1998).
18
C
Figure 2.4: (a) Structures of α-/β-, γ- and ω-gliadins and B-, C- and D-low molecular weight (LMW) glutenin subunits, (b) Structures of x- and y-type high molecular weight (HMW) glutenin subunits (Tatham et al., 1990; Shewry et al., 2009) and (c) Structural model of inter-chain disulfide bonds linkage for LMW and HMW glutenin subunits (Wieser et al., 2006).
a b
Figure 2.5: (a) Gene loci on the group 1 and 6 chromosomes for main wheat proteins (gliadins and glutenins) (Payne et al., 1987; Singh and Shepherd, 1988) and (b) Allelic variation in HMW glutenin subunits at three gene loci Glu-A1, Glu-B1 and Glu-D1 according to SDS-PAGE results and their relationship to bread quality (Payne et al., 1984). 19
2.6.2 Glutenins
Compared with gliadins, polymeric glutenins are large proteins with molecular weights ranging approximately from 60,000Da for individual subunits to more than 10 million for aggregated glutenin macropolymer. Glutenins are not soluble in common solvents
(Koehler and Wieser, 2013). Aggregated glutenins are made up of a mixture of disulfide- linked polymers. Both intra- and inter-molecular disulfide bonds exist within and between glutenins. Through the use of a reducing agent, their building blocks – glutenin subunits (GS) can be obtained (Delcour et al., 2012). Base on the SDS-PAGE mobility,
GS are divided into two groups: high molecular weight GS (HMW-GS, 70,000 to 90,000) and low molecular weight GS (LMW-GS, 30,000Da to 45,000Da) (Wieser, 2007). For the HMW-GS, two types including x-type and y-type can be distinguished (Figure 2.4b).
Both x-type and y-type contain three typical domains consisting of a nonrepetitively hydrophobic N-terminal domain with 80 to 105 residues, a repetitively hydrophilic central domain and a hydrophobic C-terminal domain with 42 residues (Shewry et al.,
1992). As a result of their specific domain structure, glutenins have low solubility coupled with high hydration capacity. All the cysteine residues that engage in intra- or inter-chain disulfide bond formation are located on the ends of the HMW-GS (N- and C- terminals). HMW-GS are believed to have a rigid rod-like conformation due to their amino acid composition and specific structure (Shewry et al., 1992). LMW-GS represent about 60% of total glutenins (Gianibelli et al., 2001). They are classified as B-, C- and D- type according to their mobility in gel electrophoresis (Figure 2.4a) (Payne and Corfield,
1979). B-type proteins, the most abundant proteins in LMW-GS, are subdivided into three groups in terms of their first amino acid of the polypeptide, including LMW-s, 20
LMW-m and LMW-i (Tao and Kasarda, 1989; Masci et al., 1995). C-type proteins are very similar to α-/β- and γ-gliadins with additional unpaired cysteine residues that likely used for inter-chain disulfide bond formation. Similarly, D-type proteins are related to ω- gliadins with extra cysteine residues (Tao and Kasarda, 1989). A basic model of glutenin polymer has been identified with the cross-linking of 6 HMW-GS and 30 LMW-GS
(Figure 2.4c). However, the polymerization might be larger than this (Wieser et al., 2006).
Although HMW-GS are minor components (5-10%) in the gluten protein family, they are key factors in determining gluten elasticity and hence affect dough characteristics (Field et al., 1983; Tatham et al., 1985). As a consequence, the genes encoding HMW-GS are of special interest. Similar to gliadins, six loci are known as the glutenin-encoding loci (Glu).
Glu-1 loci (Glu-A1, Glu-B1, Glu-D1) are located on the long arms of the group 1 chromosomes, controlling the synthesis of HMW-GS. Glu-3 loci (Glu-A3, Glu-B3, Glu-
D3), regulating LMW-GS, are linked to Gli-1 loci (Figure 2.5a) (Gianibelli et al., 2001).
Each Glu-1 locus contains two linked genes that encode x- and y-type HMW-GS (Payne,
1987). Generally, only three to five genes are expressed in hexaploid wheat due to the silencing of some Glu-1 genes (Bonomi et al., 2013). Figure 2.5b summarizes the allelic variation of HMW-GS at three loci, in which Glu-D1 plays a particularly important role.
The number represents the order of identification and the position is SDS-PAGE dependent. The arrow on the bottom represents the relationship of allele and bread making quality. For example, the pair of 1Dx5+1Dy10 generally confers greater dough strength than the pair 1Dx2+1Dy12 (Gianibelli et al., 2001). This is because of the additional cysteine residues located on the central domain of 1Dx5 subunits (Anderson 21 and Green, 1989; Kasarda, 1999), which favors the formation of larger aggregates, therefore improving gluten elasticity (Tatham et al., 1985; Gupta and MacRitchie, 1994;
Veraverbeke, 2002; Feeney et al., 2003).
To compare the molecular weight distributions of different protein compositions in wheat flour (i.e. relative amount of HMW-GS), size exclusion high-performance liquid chromatography (SE-HPLC) is commonly applied (Ciaffi et al., 1996). All the proteins are eluted on the basis of their size. Due to the low solubility of large molecular weight glutenin polymers, they remained insoluble upon being introduced to 0.5% SDS aqueous solutions. The insoluble glutenins can be isolated as large unextractable polymeric proteins (UPP) when subsequently sonicated in 0.5% SDS as a result of mechanical disruption of interchain glutenin bonding (Gupta et al. 1993). Percentage UPP (%UPP =
UPP / Total polymeric protein in both extractions) indicates the relative amount of
HMW-GS. Therefore dough with higher %UPP is associated with better elasticity (Field et al., 1983; Gupta et al., 1992; Kuktaite et al., 2004).
SDS-PAGE is traditionally employed to identify HMW-GS composition of wheat (Huang et al., 1997). It acts as a robust and fundamental technique in wheat quality testing.
However, a more efficient method, microfluidic electrophoresis (ME), is developed recently and works as an alternate way of detecting different alleles of HMW-GS
(Uthayakumaran et al., 2006). Compared with SDS-PAGE, ME is a fast chip-based technique that provides excellent reproducibility of GS patterns. ME requires less reagent and low sample volumes (microliter-scale) (Bradova and Matejova, 2008). Fast reagent 22 kits are offered by Agilent and Bio-rad. Besides SDS-PAGE and ME, HMW-GS can also be characterized by RP-HPLC or capillary electrophoresis (Bietz, 1983; Marchylo et al.,
1989; Lookhart and Bean, 1996; Bean et al., 1998; Sutton and Bietz, 1997).
Molecular weight distribution (MWD) of gluten proteins is an important factor in determining rheological properties of wheat dough (Koehler and Wieser, 2013). The largest polymeric glutenins are known as glutenin macropolymers (GMP). They are insoluble in 0.5% SDS and are made up of high ratios of HMW- to LMW-GS, in which the pair of 1Dx5+1Dy10 HMW-GS shows higher GMP concentration compared with the pair 1Dx2+1Dy12 (Koehler, 2010). The contents of GMP in wheat flour are strongly related to dough strength and loaf volume (Koehler and Wieser, 2013).
2.7 Wheat carbohydrates
2.7.1 Starch
Starch is the most abundant storage carbohydrate in wheat and an important part of nutrition in human diets, constituting 60% - 70% of the total mass of refined wheat flour
(Lineback and Rasper, 1988). In wheat kernels starch only occurs in the endosperm and is packed in granular form. Starch granules are formed in amyloplasts. In wheat, each amyloplast contains one granule (Delcour and Hoseney, 2010). There are two different types of starch granules in wheat: large and small. The larger granules are lenticular and
20 - 35μm long, whereas the smaller granules are spherical and about 2 - 10μm in diameter (Stone and Morell, 2009; Delcour and Hoseney, 2010). Starch has unique 23 characteristics because of its granular packing and the presence of both linear and branched polymers within each granule. Their difference in molecular structure contributes to the textural properties in baked foods (Goesaert et al., 2005; Zeeman et al.,
2010).
Starch polymers are built from D-glucopyranose (C6H12O6). Two types of glycosidic linkages α-1,4 and α-1,6 can be found between glucose units within the polymers. α-1,4 results in a linear linkage while α-1,6 accounts for the branched linkage, by which the two homoglucans, amylose and amylopectin, are created (Figure 2.6) (Hizukuri et al.,
1981). Amylose is a linear polymer containing 500 to 6,000 glucose units with a low branching frequency < 1% (molecular weight of 104 - 106Da). In contrast, amylopectin is highly branched with α-1,6 bonds frequencies 3% - 4% (Stone and Morell, 2009). It consists of 30,000 to 3000,000 glucose units and has a higher molecular weight than amylose (107 - 109Da) (Shibanuma et al., 1994; Atwell, 2001b). Each amylose or amylopectin molecule has only one reducing end where C1 on the terminal glucose residue is not bound to another glucose (Atwell, 2001b; Delcour and Hoseney, 2010).
24
Figure 2.6: Molecular structure of starch polymer: (a) amylose and (b) amylopectin (Pérez et al., 2009).
In normal starch, amylose and amylopectin typically occurs in about a 1:3 ratio (Colonna and Buléon, 1992). However, amylose content can vary between species and between different genotypes within species. In hexaploid wheat amylose synthesis is controlled by three waxy genes Wx-A1, Wx-B1, and Wx-D1 that regulate the production of Wx-A1,
Wx-B1, and Wx-D1 proteins (Maningat et al., 2009). The absence of Wx proteins results in reduced amylose content (Yamamori et al., 1994). For example, ‘waxy’ cultivars lack all three waxy genes, and therefore have a very high amylopectin level (approaching
100%). The amylose content has profound effects in starch behavior and end products texture. For example ‘high-amylose’ maize cultivars with up to 70% amylose, are suggested in enzyme-resistant starch production because amylose has a rapid rate of retrogradation than amylopectin (Van Hung et al., 2006; Jonnala et al., 2010). The relative proportions of amylose and amylopectin can be determined as a result of their different abilities to bind with iodine. Amylose can bind 20% of its mass in iodine, while amylopectin (highly branched) can only bind < 1%. Amylose (mainly long chains) causes blue color in starch-iodine solution, whereas amylopectin (mainly short chains) results in 25 reddish color (Stoddard, 2004). This is due to the polyiodide ions locating in the hydrophobic center of amylose single helix structure.
Starch granules show birefringence in polarized light, which indicates that they have a high degree of molecular order. Based on microscopic and X-ray diffraction studies, starch granules are semicrystalline materials (Figure 2.7). Amylose and the less ordered amylopectin branching regions are in an amorphous state, while crystalline regions are made up of amylopectin crystalline lamellae (amylopectin double helices) (Delcour and
Hoseney, 2010). Together these structures lead to the alternating, periodic structure seen in Figure 2.7. In general, normal starch has an up to 36% crystallinity compared with waxy starch 45% (Maningat et al., 2009)
Figure 2.7: Semicrystalline structure of starch granule (Donald et al.,1997; Buléon et al., 1998).
Starch granules are insoluble in cold water, but they do absorb some water (BeMiller and
Huber, 2007). At ambient temperature, starch granules can take up to 30% of their dry weight in water leading to a 5% increase in volume and a limited swelling (Delcour and
Hoseney, 2010). This limited swelling is reversible. Starch granules that are physically damaged during milling absorb about twice amount of water as their own weight 26
(Cauvain and Young, 2001). In contrast, when a starch suspension is heated in excess water (water:starch ratio > 1.5) (Buléon and Colonna 2007), water penetrates into the granules and eventually destroys the molecular order. This irreversible endothermic process is called gelatinization (Atwell et al., 1988). Several changes occur during this hydration reaction that precede the increase in viscosity known as “pasting”: transition from glassy to rubbery state of the amorphous regions, dissociation of amylopectin double helix crystallites (Tester and Debon, 2000; Waigh et al., 2000), loss of birefringence, swelling and distortion of granules, and a progressive solubilization of starch (primarily amylose leaching) (Kalichevsky and Ring, 1987). The gelatinization temperature is temperature at which the loss of birefringence occurs. This temperature generally covers a wide range, which is determined by starch: water ratio, granule type, heterogeneity within the granules, and the presence of other molecules like lipids and sugars (BeMiller and Huber, 2007).
Viscometry is commonly used to monitor the gelatinization process through observation of viscosity change. Onset temperature (initiation of the process), peak as well as conclusion temperatures are the three characteristic points on a standard pasting curve
(Eliasson and Gudmundsson, 1996).
One example of viscometric observation of starch gelatinization/pasting is the Rapid
Visco Analyzer (RVA) system. In this system a stirring paddle provides the shear force.
The starch slurry is continuously heated beyond the gelatinization temperature where the granules continue to swell and burst. Amylose leaches from the broken granules and, 27 eventually total disruption of starch granules occurs under the shear force (BeMiller and
Huber, 2007). This process is defined as pasting (Batey, 2007). Starch paste is formed as a viscous mass with swollen granules and solubilized macromolecules (mainly amylose)
(Eliasson and Gudmundsson, 1996; Tester and Debon, 2000). In native starch, pasting temperature is dependent on water content, amylose/amylopectin ratio and the presence of other molecules like lipids (Tester and Morrison 1990; BeMiller and Huber, 2007).
On cooling, amylose and amylopectin partially reassociate via the formation of junction zones (zones where polymers are linked by non-covalent bonds), transforming the amorphous starch paste to a more ordered and crystalline state. This process is termed retrogradation (Awell et al., 1988). In starch, junction zones are mediated by H-bonds.
Junction zones create the three-dimensional gel network, while their further extension results in an increase in rigidity. Generally, amylose retrogradation determines the initial hardness of a gel, whereas amylopectin retrogradation accounts for the long-term gel structure (Mile et al., 1985). The 3D gel network is beneficial in structural support of baked products, but its increasing rigidity is normally undesirable as explained below. As the junction zones continue to grow, a more compact network forms and such contraction leads to the release of entrapped water. This is called syneresis (BeMiller and Huber,
2007).
Starch polymers are able to interact with other molecules (particular lipids) due to their unique conformation. Amylose is a linear chain with single left-handed helix structure, where there is a central hydrophobic domain that allows the formation of helical 28 inclusion complexes with polar lipids (French and Murphy, 1977). The amylose-lipid complexes prevent the leaching of amylose and the water entry into the granule (Stoddard,
2004), resulting in a delay of the gelatinization onset temperature. Since the amylose- lipid complexes do not participate in amylose-amylose recrystallization, retrogradation is also delayed (Eliasson and Gudmundsson, 1996). The lipid complexing phenomenon can be found in the outer branches of amylopectin as well (Morrison et al., 1993).
2.7.2 Non-starch polysaccharides
In wheat grain, most of non-starch polysaccharides (NSP) are located in the bran, and they make up to 75% of the dry weight of endosperm cell walls. Wheat NSP include arabinoxylans (AX), β-glucan, cellulose and arabinogalactan-peptides (AGP) (Mares and
Stone, 1973). Compared with starch polymers, NSP have different monosaccharide compositions and/or different linkages (Henry, 1985). From a nutritional perspective,
NSP are dietary fiber because they are indigestible in the human gastrointestinal tract.
Positive health effects like improving chronic diseases, type II diabetes and gastrointestinal cancer have been attributed to these NSP (Blackwood et al., 2000; Stone and Morell, 2009).
2.7.2.1 Arabinoxylan (AX)
85% of NSP in wheat is made up of AX. Endosperm cell walls are predominantly made of AX. (Mares and Stone, 1973). Since the building blocks of AX are arabinose and xylose (pentose sugars), they are often known as pentosans (Saulnier et al., 2007). AX are composed of a basic D-xylopyranosyl backbone linked through β-1,4 glycosidic bonds, 29 substituted at O-3 or both O-2 and O-3 positions with single α-L-arabinofuranosyl units
(Perlin, 1951; Meuser and Suckow, 1986; Stone and Morell, 2009). Ferulic acid can be bound to arabinose molecules at O-5 position through an ester linkage (Figure 2.8)
(Fausch et al., 1963; Courtin and Delcour 2002). Wheat AX can be subdivided into two fractions in terms of their extractability: water-extractable (WEAX) and water- unextractable (WUAX). Between 1/4 to 1/3 of total AX in wheat are WEAX, which have positive functionality in bread-making due to their water-absorbing properties (Meuser and Suckow, 1986; Izydorczyk and Biliaderis, 1995). WEAX are made up of alternating highly branched and open regions, resulting in the variation in arabinose-to-xylose (A/X) ratio of different sub-fractions (ranging from 0.31 to 1.06) (Dervilly et al., 2000). This
A/X ratio is of special interest since it determines the AX conformation and functionality in wheat (Kiszonas et al., 2013). WUAX can be extracted with alkaline solution through ester-linkage breaking, yielding structures comparable to those of WEAX (Gruppen et al.,
1992; Gruppen et al., 1993; Shelton and Lee, 2000; Courtin and Delcour 2002).
30
a b
c d
Figure 2.8: Chemical structure of arabinoxylans (AX). (a) Unsubstituted D-xylopyranosyl residue, (b) monosubstituted AX at O-3 with a ferulic acid moiety on the arabinose molecule, (c) monosubstituted AX at O-2 with α-L-arabinofuranosyl residue and (d) disubstituted AX at both O-2 and O-3 (Izydorczyk and Dexter, 2008).
The properties of AX are primarily ascribed to their water-holding and water-binding capacities (Jelaca and Hlynca, 1971). AX can absorb more than 15-20 times its dry weight in water (Gan et al., 1995). This ability can be further increased under oxidizing conditions in a process known as ‘oxidative gelation’, resulting in the three-dimensional gel networks via oxidative cross-linking of ferulic acid on the adjacent AX molecules
(Izydorczyk et al., 1990; Vinkx et al., 1991; Bettge and Morris, 2000; Stone and Morell,
2009).
In hard wheat products like breads, WEAX have beneficial effects in difference stages
(Ramseyer et al., 2011; Kiszonas et al., 2013). The strong hydration capacity of WEAX influences the rheological properties of dough by increasing consistency & stiffness and decreasing extensibility (Jelaca, and Hlynca, 1972). WEAX-gluten protein or WEAX-
WEAX may form a covalent gel network, which further reinforces the gluten network 31
(Jelaca, and Hlynca, 1972). The rigid network slows the diffusion of CO2, so as to increase gas retention and stabilize dough foam during the fermentation process
(Hoseney, 1984; Gan et al., 1995). Upon baking, WEAX associated with to a higher loaf volume and improved crumb homogeneity due to stabilize the gas cells (Gan et al., 1995).
Although WUAX possess a strong water-absorbing capacity, they cause detrimental effects on hard wheat products by forming physical barriers against gluten development or stimulating gas cells coalescence (Courtin et al., 1999; Courtin and Delcour 2002).
However, baking performance can be improved by using endoxylanases to hydrolyze
WUAX to enzyme-solubilized AX (Verjans et al., 2010; Zheng et al., 2011).
2.7.2.2 β-Glucan
β-Glucans are linear, unbranched polymers located in most cereal grain cell walls. They are present particularly in barley and oats, at 3-7% and 3.5-5% respectively (Koehler and
Wieser, 2013). While in wheat, only < 1% β-glucans can be found, accounting for 29%
(w/w) of aleurone walls and 20% (w/w) of starchy endosperm cell walls (Bacic and Stone,
1981a; Bacic and Stone, 1981b; Beresford and Stone, 1983). The chemical structure of β- glucan is composed of β-D-glucopyranosyl residues linked through both β-1,3 and β-1,4 glycosidic bonds. The average molecular weight of β-glucans after purifying from wheat bran is around 487,000 (Li et al., 2006). In general, β-glucans are soluble in water and can form highly viscous solutions due to their linear and flexible chain conformation
(Buliga et al., 1986). From nutritional point, β-glucan is a source of dietary fiber that is able to reduce postprandial glycemic and insulinemic responses (Wood, 2007).
32
2.7.2.3 Cellulose
Cellulose can be found in the cell walls of all cereal grains. Most of the cellulose (30% w/w) is located in the pericarp, while only about 2% (w/w) are present in the aleurone walls and < 0.3% in the starchy endosperm cell walls (Fraser and Holmes, 1959; Stone and Morell, 2009). Like β-glucan, celluloses are linear polymers consist of β-D- glucopyranosal units, but they are only joined by β-1,4 linkages (Shelton and Lee, 2000).
This ribbon-like conformation allows the self-association of adjacent chains via hydrogen bonds and van der Waals interactions (Stone and Morell, 2009). Therefore, celluloses are considered insoluble dietary fiber due to their propensity to form water-insoluble aggregates.
2.7.2.4 Arabinogalactan-peptides (AGP)
Arabinoglactan-peptides (AGP) concentration varies from 0.27 to 0.38% (dw) in wheat grain (Ingelbrecht et al., 2001). The peptide backbone in wheat flour of AGP is made up of 15 residues with three hydroxyproline units. Each of these unit attaches to a β-1,3 linked D-galactopyranan sub-backbone with branched β-1,6-linked D-galactose short chains on it. These short branched chains can subsequently linked to L-arabinose units through α-1,3 glycosidic bonds (Strahm et al., 1981; Loosveld et al., 1998; Van den
Bulck, 2002). The L-arabinose to D-galactose ratio of an AGP molecule ranges from 0.66 to 0.73 (Loosveld et al., 1998). In wheat, AGP are considered to be water soluble NSP
(Fincher and Stone, 1974). Substitution with 1% or 2% of flour with AGP causes a reduction in baking absorption, dough extensibility and bread volume (Loosveld and
Delcour, 2000). 33
2.8 Wheat lipids
Lipids are minor constituents that derived from membranes, organelles and spherosomes in wheat grain (Goesaert et al., 2005). They vary in physical properties and chemical structures. The distribution of crude fats in the whole-wheat grain is shown in Table 2.1.
Most of the lipids are located in germ (mainly non-polar triglycerides), while some lipids are found in bran or endosperm (mainly polar glycolipids and phospholipids) (Morrison,
1978; Delcour and Hoseney, 2010).
Table 2.1: Distribution of crude fats in whole-wheat grain (Morrison, 1978).
Tissue Proportion of Whole Kernel (%) Crude Fat (%) Whole grain 100 2.1 – 3.8 Bran n/a 5.1 – 5.8 Pericarp 5.0 – 8.9 0.7 – 1.0 Testa, hyaline 0.2 – 1.1 0.2 – 0.5 Aleurone 4.6 – 8.9 6.0 – 9.9 Endosperm 74.9 – 86.5 0.75 – 2.2 Scutellum 1.1 – 2.0 12.6 – 32.1 Embryonic axis 1.0 – 1.6 10.0 – 16.3
Based on extractability, wheat lipids can be classified as starch lipids, and free or bound non-starch lipids (Pareyt et al., 2011). About 75% of total lipids in wheat endosperm are non-starch lipids (MacMurray and Morrison, 1970). They comprise approximately 60% nonpolar lipids, 24% glycolipids and 15% phospholipids (Delcour and Hoseney, 2010).
The nonpolar lipids are primarily present in the free form, while glycolipids or phospholipids can be associated with proteins and therefore present in the bound form
(Eliasson and Larsson, 1993). In contrast, the starch lipids contain 9% nonpolar lipids, 5% glycolipids and 86% phospholipids, in which 85% of the phospholipids are 34 lysophosphatidylcholine (Wrigley et al., 2009; Delcour and Hoseney, 2010). They are able to form helical inclusion complexes with amylose and play an important role in starch gelatinization behavior (see above 2.7.1). Considering the fatty acid composition, linoleic acid (C18:2) is the predominant group with small amounts of oleic acid (C18:1) and palmitic acid (C16:0) (Morrison, 1978).
Although wheat lipids are present in minor quantities, they are greatly influence baking performance. It is reported that only polar lipids have beneficial effects on bread making quality, while nonpolar lipids are detrimental (Morrison et al., 1975). For example, polar lipids (interact with proteins) increase the gas holding capacity and eventually improve the loaf volume and crumb resilience (Matsoukas and Morrison, 1991; Gan et al., 1995).
2.9 Wheat enzymes
Starch, non-starch polysaccharides, proteins and lipids are the major targets of hydrolytic enzymes in wheat kernel. Particularly upon germination, either through undesired pre- harvest sprouting or deliberate germination in malting, many hydrolytic enzymes are synthesized (Brijs et al., 2009). However, a group of protein inhibitors, which are supposed to resist pathogenic microorganisms or pests, can endogenously inhibit these hydrolytic enzymes (Shewry and Lucas, 1997). Furthermore, oxidoreductases are another important enzyme category in wheat grain that affect baking performance (Drapron and
Godon, 1987; Kruger and Reed, 1988). In this review the focus is on starch- and protein- degrading enzymes and some of the oxidoreductases, such as Polyphenol oxidases (PPO) and Peroxidases (POD). 35
2.9.1 Starch-degrading enzymes
Glycoside hydrolase family 13 (α-amylase family) enzymes are responsible for hydrolyzing α-1,4 and α-1,6 linkages of starch polymers. A-amylases (EC 3.2.1.1) are endo-enzymes that randomly hydrolyze α-1,4 linkage and degrade starch into α-dextrins, rapidly decreasing viscosity building capacity of the starch. A-amylases are also known as liquefaction enzymes. Maltogenic or malto-oligosaccaride producing amylases and debranching amylases are also included in family 13 amylolytic enzymes. Debranching amylases like isoamylase and pullulanases, are capable of hydrolyzing α-1,6 linkages, hence removing side chains. In addition, glycoside hydrolase family 14 (β-amylases, EC
3.2.1.2) also works in starch hydrolysis. B-amylases are exo-acting enzymes that only hydrolyze α-1,4 linkage at the non-reducing ends of starch polymers and yield β-maltose or β-limit dextrins (Bowles, 1996; Delcour and Hoseney, 2010). As a result of their production of fermentable sugars β-amylases are known as enzymes of saccharification.
In consequence, these enzymes reduce the viscosity of starch slurry and are important for the textural properties of end products.
Pre-harvest sprouting is the major source of detrimental α-amylase activity in wheat and tests are required to monitor α-amylase activity of grain deliveries in rain-affected harvests. Tests for α-amylase activity fall into the following categories: visual assessment, autolytic tests, and direct α-amylase or enzymatic proteins measurements (Ross and
Bettge, 2009). Falling Number (FN) and the RVA have been widely used in analyzing starch viscosity, which can act as a secondary measurement of amylase activity. The
RVA Stirring Number test (AACC-I Approved Method 22-08.01) is quick and can be 36 easily customized, which is performed by stirring a wheatmeal suspension at 95℃ for 3 minutes. A sample with high amylase activity results in rapid viscosity loss and low final viscosity (Figure 2.9) (Bason and Blakeney, 2009). FN (AACC-I Approved Method 56-
81.03) is another robust method in determination of sprout damage. The pre-mixed samples are added into FN tubes, and then placed in boiling water. A 60 sec instrumental mixing follows this, after which the stirrer starts to drop through the flour slurry. The amount of time it takes for the stirrer to drop to the bottom of the tube is recorded. A sample with sprout damage or high amylase activity will have a lower FN because of lower paste viscosity. Wheat samples with FN > 300 sec are generally agreed to be sound
(Perten, 1964).
Figure 2.9: Comparison of stirring number curves with standard RVA curves according to three different FN wheatmeals. Temperature profiles are at the top (Bason and Blakeney, 2009).
2.9.2 Protein-degrading enzymes
Protein-degrading enzymes are referred to as ‘proteases’. Proteases are able to degrade wheat proteins by cleaving peptide bonds. Both endo- and exo-proteases exist. Endo- proteases hydrolyze the intermediate peptides (resulting in two peptides), while exo- 37 proteases target at the N- or C- terminal of the peptides (resulting in one peptide plus a single amino acid) (Delcour and Hoseney, 2010).
Generally, four types of proteases (EC 3.4.21-24) are present in wheat grain according to their catalytic mechanisms (Brijs et al., 2009). 1) Aspartic proteases have two aspartic acid residues in their active site, and are very active in hydrophobic environment at acidic pH (Kawamura and Yonezawa, 1982). 2) Serine proteases have a hydroxyl group at the active site, and prefer to react at alkaline pH (pH 7.5 to 10.5). They are abundant at the early stages of wheat kernel development. 3) Metalloproteases require a metal ion present in their active site, and these are abundant later stages of wheat kernel development
(Dominguez and Cejudo, 1995). 4) Cysteine proteases are involved in ‘nucleophile- electrophilic catalysis’. The nucleophile is the sulfhydryl group on cysteine residue, with the presence of a proton donor (Histidine residue) (Brijs et al., 2009).
The presence of proteases hydrolyzes the polypeptide chains, and so as to break the supportive structure (gluten) of dough that might cause dough collapse.
2.9.3 Oxidoreductases
Polyphenol oxidases (PPO) (EC 1.10.3.1-2) are copper enzymes able to catalyze enzymatic browning (Nicolas et al., 2003). PPO can be classified into two types; catechol oxidases and laccases. In the presence of oxygen, oxidases are capable of catalyzing two kinds of reactions including hydroxylation (monophenols → o-diphenols) and oxidation
(o-diphenols → o-quinones), while laccases can trigger oxidation (o- / p-diphenols → o- / 38 p-quinones) via intermediate semiquinones (Brijs et al., 2009). The resulted quinones are very reactive and lead to the formation of polymerized melanoidin pigments. Therefore, the level of PPO greatly affects the color of the wheat-based end products. In addition, the dough rheological properties can also be affected as a result of the interaction between gluten proteins and phenols (Hoseney and Faubion, 1981; Labat et al., 2000).
Peroxidases (POD) (EC 1.11.1.7) are hematin enzymes involved in hydroxylation, peroxidatic and oxidative reactions (Whitaker, 1985; Robbinson, 1991). Peroxidatic reactions require a hydrogen donor (phenolic compounds) and the presence of hydrogen peroxide. Hydroxylation and oxidative reactions are much slower due to the formation of semiquinones (Dunford and Stillman, 1976; Brijs et al., 2009). POD has profound effects on dough rheological property because of the enhancement in gluten polymerization
(Dunnewind et al, 2002).
2.10 The flour-water system during processing
Starch and protein are the primary components in wheat flour. When water is added to wheat flour during processing to form a dough or batter, flour-water system undergoes several physical and chemical changes. Three main things happen during production of wheat-based products (breads, noodles, etc.); hydration, shear (mixing) and heat (baking or cooking) (Delcour et al., 2012). Starch-water interactions are discussed in section 2.7.1.
When coupled with the presence of different gluten proteins (content and composition) the starch-water interaction can differ significantly, resulting in profound effects on the texture of end products (Kovacs, et al., 2004; Singh et al., 2011). 39
In general, gluten-water behavior has three stages that lead to the formation of a viscoelastic mass: hydration, gluten development, and cross-linking (Delcour et al., 2012).
Upon hydration, gluten proteins transit from the glassy state to a rubberlike state, and their glass transition temperature decreases as water (plasticizer) content increases
(Hoseney et al., 1986). Hydration with the plasticizer water allows the gluten proteins to react or interact (Slade et al., 1989; Levine and Slade, 1990). On a molecular scale, glutenin elasticity is because of its intrinsic elasticity of β-spiral HMW-GS and can also be explained either by a loop and train model (Figure 2.10) (Belton, 1999) or a polymer entanglement model (MacRitchie, 1999). For the protein-protein interactions (trains), glutamine residues on individual glutenin chains (especially HMW-GS) are connected via inter-chain hydrogen bonds. For protein-water interactions (loops), hydrogen bonding between glutamine and water breaks the protein train model connections, resulting in the loop regions. More loops are formed with increased hydration, but further extension of dough provides sufficient protein inter-chain connections and maintains the stiffness. It is believed that glutenin elasticity is regulated by these non-covalent interactions (Belton,
1999). MacRitchie (1999) proposed another polymer entanglement model to describe glutenin elastic structure. He suggested that glutenin aggregates are physically entangled, and they cannot be separated from covalent junctions if last longer than the required time to pull apart. The entanglement of glutenin subunits and their high molecular weight contribute to dough continuity. However, gliadins, which contribute to dough viscosity, are able to weaken this glutenin structure and work as a plasticizer (Veraverbeke and
Delcour, 2002). Thus, the overall viscoelasticity of dough is determined by balancing both glutenins and gliadins. 40
Figure 2.10: Loop and train model to explain glutenin elasticity. (1) Equilibrium state, (2) Small extension and (3) Large extension (Belton, 1999).
Further gluten development arises with the input of mechanical energy at ambient temperature. During dough mixing, rearrangement of covalent disulfide bonds occurs between glutenin molecules under extension, resulting in a rigid glutenin network aligned along the extension direction (Shewry et al., 2000). However, continued energy input eventually causes physical destruction to the dough network. In contrast, the intra- molecular disulfide bonds located in gliadin molecules are very stable, so that they are not involved in SH-SS interchanges (Delcour et al., 2012).
Heating breaks hydrogen bonds in wet gluten and increases network mobility (Guerrieri et al., 1996; Lefebvre et al., 2000). Generally, gluten structure changes above 40℃
(Delcour et al., 2012). It is reported that the exposure of hydrophobic groups and the formation of cross-links occur at approximately 45℃ and 50℃ respectively (Guerrieri et al., 1996). Under such condition, SH-SS interchanges happen in both glutenins and gliadins. Continued heating results in protein polymerization, in which covalent disulfide linkage is the key factor (Lagrain et al., 2005). The oxidation of sulfhydryl groups into 41 disulfide bonds causes the polymerization of glutenin subunits, as well as the aggregation between gliadin and glutenin molecules above 90℃ (Weegels et al., 1994; Singh and
MacRitchie, 2004; Lagrain et al., 2008). Although only a few amino acids contain sulfhydryl groups, the overall conformation experiences a huge change. Besides SS bonds, cross-linking occur in other amino acids such as the formation of dehydroalanine
(DHA) and lanthionine (LAN) at pH ≥ 6 (Rombouts et al., 2010).
Taking starch into account, the characteristics of gluten proteins have strong correlations with pasting, gel viscoelasticity and dough properties (Singh et al., 2011). Singh and coworkers (2011) reported that flour with high protein content showed lower pasting temperature, peak viscosity and paste breakdown. The presence of protein might prevent the water uptake of starch granules and hence restricting swelling. Also, the heat-setting of gluten protein could support the whole matrix and slow down the starch thixotropism
(Fitzgerald et al., 2003). Moreover, the viscoelasticity of a flour gel is affected by the formation of starch-protein gel network (primarily starch-UPP) rather than the amylose content (Singh et al., 2011). Kovacs and coworkers (2004) reported that there was a significant positive relationship between gluten thermal stability and dough properties.
They suggested that lower ratio of gliadins and LMW-GS to HMW-GS decreased the gluten thermal stability and accelerated their denaturation. If protein denature prior to the disintegration of starch granules, the formation of a rigid protein network might prevent the starch granules from water uptake enabling the granules to maintain their integrity, resulting in a chewy and firm texture of noodles (Kovacs et al., 2004).
42
2.11 Gluten intolerance
Celiac disease (CD) is an immune-mediated gut disorder initiated by the ingestion of gluten-containing grains such as wheat, barley, rye and possibly oats (Fasano et al., 2003;
Wieser and Koehler, 2008; Shewry, 2009; Sapone et al., 2012). Clinical symptoms include abdominal pain, anemia, weight loss, failure to thrive, vomiting, diarrhea, etc. with continued gluten consumption (Tonutti and Bizzaro, 2014). It has been reported that the prevalence of CD is 0.71% in the US and similar numbers are found in European countries (Rubio-Tapia et al., 2009; Mustalahti et al., 2010). CD has been demonstrated to be strongly associated with human leukocyte antigen (HLA) DQ2 and DQ8 (MHC class II). HLA-DQ2 antigen is found in approximately 95% of CD patients, among which
10% of them express both HLA-DQ2 and -DQ8 antigens (Karell et al., 2003).
CD is triggered by specific regions of α-gliadins. Many studies have focused on a 33-mer immunogenic peptide located at the N-terminal, amino-acid number 57-89 region (Shan et al., 2002). This 33-mer can be digested by intestinal enzymes but is resistant to further degradation by brush border enzymes, and becomes a good substrate for tissue transglutaminase (tTG) (Shan et al., 2002; Mowat, 2003; Tonutti and Bizzaro, 2014). tTG catalyzes deamidation, converting glutamine to glutamate residues. This results in a tight connection between the deamidated gliadin peptide and HLA-DQ2 or -DQ8 complexed with antigen presenting cells (APC) (Dewar et al., 2004; Meresse et al., 2009; Tonutti and
Bizzaro, 2014). Meanwhile, CD4+ T cells are activated after peptide recognition by T cell receptors (TCR) (Wieser et al., 2012). This T cell stimulation causes immune responses and secretes the inflammatory cytokines, which result in villus atrophy (Th-1 43 response). In addition, activated T cells facilitate the maturation of B cells that are responsible for production of IgA and IgG antibodies (Th-2 response) (Dieterich et al.,
1997; Fleckenstein et al., 2004; Wieser et al., 2012). These antibodies are used in particular for serological tests in CD diagnosis. CD diagnosis is commonly conducted by detection of IgA anti-tTG and anti-endomysial antibodies (specific antibody for gluten).
CD can also be assessed by biopsy that directly observes the varying degrees of villus atrophy (Tonutti and Bizzaro, 2014).
Recently, another gluten-associated disorder has been suggested where patients have negative CD test results, exhibit no duodenal mucosa damage, but share some, but less severe, CD symptoms like abdominal pain, anemia, headache, fatigue, diarrhea, etc.
(Sapone et al., 2012; Brouns et al., 2013; Khamsi, 2014). This disorder is termed “non- celiac gluten sensitivity” (NCGS). There is currently no clinical method of diagnosing
NCGS. It can only be distinguished by determination that the patient does not have either
IgE-mediated wheat allergy or CD, in parallel with the observation of gluten-induced symptoms and the potential efficacy of a gluten-free diet (GFD) (Sapone et al., 2012;
Tonutti and Bizzaro, 2014). There is controversy. Some researchers claim that an increasing number of people are eliminating gluten from their diets without adequate cause and some are also casting doubt on whether NCGS even exists (Biesiekierski et al.,
2011; Carroccio et al., 2012; Khamsi, 2014). Based on double-blind placebo-controlled surveys, it is still under debate whether “NCGS” is associated with gluten or other wheat components (e.g. fermentable carbohydrates known as fermentable oligo-, di-, and monosaccharides and polyols: FODMAPs, or amylase trypsin inhibitors) (Barrett and 44
Gibson, 2012; Junker et al., 2012; Biesiekierski et al., 2013). Similarly, NCGS is shown to be associated with irritable bowel syndrome (IBS) that is triggered by FODMAPs
(Verdu et al., 2009; El-Salhy et al., 2014).
Based on evidence that suggests that the incidence of CD has increased (Rubio-Tapas et al. 2009) wheat researchers have been interested to see if there have been changes in gluten composition in the modern era. Van den Broeck et al. (2010) suggested that the increase in CD may have come from wheat breeding that inadvertently or otherwise presented a greater frequency of CD epitopes (e.g. Gliadin-α9) as a side effect of breeding for increased dough strength. Others, not wheat researchers, have also made inferences about gluten in the modern era, which have gained popular followings. Willam
Davis (2011) in the sensationalist book “Wheat Belly” speculated that the introduction of dwarfing alleles caused a change in wheat gluten proteins that negatively affected human health. However, Kasarda (2013) suggests three other possibilities, including 1) increased wheat consumption; 2) use of gluten as a food additive; 3) improved CD diagnosis.
Therefore, it is useful to confirm or refute whether gluten has fundamentally changed in the last 100 years or so (the “modern era”).
References: see Bibliography
45
Chapter 3: Investigations of Non-amylase Factors affecting Wheat Falling Number Results
Ying Zhang, Teepakorn Kongraksawech, and Andrew S. Ross
Abstract
Falling number (FN) test is widely used to determine the pre-harvest sprouting status of wheat. Normally high FN (> 300 s) indicates sound (unsprouted) wheat and low FN (<
300 sec) is considered evidence of the presence of excess α-amylase as a result of sprouting. However, wheat with low FN in the absence of elevated α-amylase has been recently reported. Grain protein content (GPC) has been implicated as a potential modifier of FN independent of α-amylase or sprout status. The aim of this study is to investigate the correlations between wheat protein responses to hydrothermal processing and FN at molecular level. We propose a protein unfolding and crosslinking model, and hypothesize that gluten proteins with higher molecular weight distributions (MWD) will heatset faster, tightly cover starch granules, restrict water entry, and slow their disintegration. In contrast to our hypothesis, our results showed that samples with lower
MWD had faster heatset times than samples with higher MWD according to a controlled heating test. We also demonstrated that natural variations in flour particle size from a standard grinding procedure had no impact on FN.
3.1 Introduction
Pre-harvest sprouting (PHS) is an undesirable phenomenon and primarily results from rain falling on harvest mature grain. Upon germination, large amounts of α-amylase (a starch-degrading enzyme) are synthesized (Mansour, 1993). However, the activity of α- 46 amylase needs to be maintained at an optimal level during bread manufacturing and as a result, in practice, millers prefer to source sound, unsprouted grain and dose α-amylase at controlled levels. This is because the presence of excess α-amylase can have a negative impact on both processing and on end-product quality: e.g. sticky doughs and poor crumb textures (Goesaert et al., 2006). These faults occur partly as a result of excessive hydrolysis of starch polymers (McFall and Fowler, 2009) although heightened proteolytic activity in PHS needs to be also taken into account.
Pre-harvest sprouting can be evaluated by a number of methods including visual assessment, autolytic tests, direct α-amylase activity assays, or measurements of the amount of enzymatic protein present (Ross and Bettge, 2009). Autolytic tests are the most widely used of the above methods in PHS evaluation and Falling Number (FN) is by far the most common autolytic method used to monitor PHS in grain trading.
Autolytic means using the endogenous starch in the grain as substrate for the α-amylase, also native to the grain. Amylase activity is then indirectly measured by observing changes in viscosity of a cooked flour water suspension, most commonly using excess water. Basically, FN measures the physical consistency of a flour paste by measuring the time in seconds for a free-falling plunger to descend a prescribed distance (AACCI
Approved Method 56-81.03, 2010). Sprout-damaged samples are expected to have lower
FNs than undamaged ones due to less resistance to the falling plunger. Wheat samples free of PHS damage generally have FN > 300 s, while a FN < 250 s usually indicates the presence of elevated α-amylase as a result of PHS (Perten, 1964; McFall and Fowler,
2009). 47
Ross et al. (2012) reported that soft wheat samples (collected from the US Pacific
Northwest) without elevated α-amylase periodically possessed FN < 300 s. Upon testing soft white winter wheat samples from the 2011 harvest, they found that GPC could be an important non-amylase factor modulating FN results. Their results showed FN was positively correlated to GPC (r = 0.65, P ≤ 0.001). The relationship became slightly stronger after removing the sprouted samples (r = 0.69, P ≤ 0.001). On the basis of analysis of variance (ANOVA), FN was mainly affected by wheat growing environment
(e.g., location) (Ross et al., 2012). Wheat with low mean GPC from particular locations resulted in low FN, and vice versa. Furthermore, a stronger correlation between GPC and
FN was observed on samples with high GPC (Ross et al., 2012). Although few studies have observed correlations between GPC and FN in the absence of PHS, there are other studies supporting the findings of Ross et al. (2012) (Zhang et al., 2004; Zhang et al.,
2005; Craven et al., 2007). In addition to GPC, Ross et al. (2012) also found a positive correlation between hardness index and FN (r = 0.45, P ≤ 0.001). This is possibly a consequence of the observation that hardness is also modulated by protein content (Pers.
Comm. Dr. Ross). Overall, GPC and hardness could be two possible non-amylase modulators of FN. Demonstration of this correlation will help helped establish a new baseline for FN testing, since sound wheat produced in environments that tend to produce low GPC grain may also produce low FN grain that may wrongly be rejected for PHS under current standards.
The aim of this study was to determine the effects of wheat protein on FN at the molecular level. To do this, underlying mechanisms of biopolymer behavior in a flour- 48 water system during wheat processing were investigated. The work could not proceed until a new model size-exclusion high performance liquid chromatography (SE-HPLC) column (BioSep-SEC-S 4000, Phenomenex, Torrance, CA, USA) with improved silica media was validated. This was needed after the removal of the old model (used by wheat chemists for over 20 years: e.g. Gupta et al., 1993) from the market. The manufacturer described the improvement to the media as a tighter particle size distribution with higher efficiency and better resolution between compounds of interests. Peak shifts and changes in retention time of the chromatogram were observed upon using the new packing. For this purpose, an adjusted unextractable polymeric protein (UPP) testing method by SE-
HPLC was developed in this study.
Two hypotheses have been tested in this study:
1. During hydrothermal processing, gluten proteins heatset. Glutenin proteins do not
denature in the classic sense (Hoseney et al. 1986), although gliadins may show
an endothermic peak related to unfolding under certain heating conditions (Noel
et al. 1995). Gluten proteins then heatset through extensive disulfide bonding on
continued heating (Schofield et al. 1983, Hayta and Schofield 2004). Based on the
literature (Kovacs et al. 2004) we hypothesized that the longer gluten
polypeptides associated with stronger dough properties are more flexible and
would therefore heatset faster than shorter ones. If gluten heatset occurs before
starch gelatinization, as suggested for the larger gluten proteins, the gluten
proteins may crosslink with each other and form a mesh-like layer around the
starch granule. In consequence, the hydration, swelling, and subsequent 49
breakdown of starch granules is slowed down due to the presence of the presumed
polypeptide outer layer thus increasing FN.
2. Milling harder grains usually results in higher mean particle size in the resultant
flour. The increased granularity of the flour reduces the surface area to volume
ratio so the starch granules embedded in the particles need more time to hydrate
or swell. By inference the starch granules also take longer to breakdown during
FN measurement and a larger FN will be obtained. Therefore, we hypothesize that
flour particle size will be another non-amylase factor affecting FN.
3.2 Materials and methods
3.2.1 Materials
Size exclusion high performance liquid chromatography (SE-HPLC): validating the new column
Grain samples were obtained from the Oregon State University, 2011 harvest, winter wheat elite yield trials. Forty-four hard wheat samples were randomly selected from six locations in Oregon, USA (Arlington, Condon, Corvallis, Hermiston, Lexington, Moro).
Whole-wheat flour was obtained by directly pouring 200 g dry kernels into a hammer mill with a 0.8 mm screen, with vacuum cleaning of the mill between samples
(Laboratory Mill 3100, Perten Instruments, Inc., Springfield, IL).
50
Controlled heating & fast heating experiments
Grain samples were obtained from the Oregon State University winter wheat elite yield trials. The samples were harvested in 2012 at several locations across. The two chosen varieties, Norwest 553 and ORSS-1757, were commonly considered to be high quality hard and soft wheats respectively (Flowers et al., 2008; Flowers and Peterson, 2010) with strong (Norwest 553) and weak (ORSS-1757) dough properties. Non-sprouted samples were selected and set in two dimensions (Table 3.1):
1 - same GPC for both varieties and
2 - a wide range of GPC within each variety
Grain samples were tempered to 15% and 14% moisture for hard and soft samples respectively. Grain was tempered for 12 h to 18 h before milling. A Brabender
Quadramat Senior laboratory mill (Quad Senior: Brabender Instruments Inc. GmbH & Co.
KG, Germany) was used to yield straight-grade flour (Jeffers and Rubenthaler, 1977).
Particle size determinations
Grain samples were again selected from the Oregon State University winter wheat elite yield trials (OWEYT) 2011 harvest. For laser particle size analysis (Mastersizer Hydro
2000s, Malvern Instrument Ltd. Malvern, UK) three varieties, Bobtail, Coda and
Brundage 96, were chosen due to the different slopes of their GPC versus FN regressions in earlier work in the Ross lab. Falling Numbers of Brundage 96 and Bobtail were responsive to changes in GPC, whereas FN of Coda was not (Regression slopes:
Brundage 96 – 23.6 seconds/percent GPC, Bobtail – 13.4 seconds/percent GPC and Coda 51
– 0.3 seconds/percent GPC). For laser particle size analysis, fifty samples were randomly selected from the lowest to highest GPC, in order to represent the whole GPC distribution.
Whole-wheat flour was produced by directly pouring 200 g dry kernels into the hammer mill with a 0.8 mm screen, with vacuum cleaning of the mill between samples
(Laboratory Mill 3100, Perten Instruments, Inc., Springfield, IL).
Other materials
All chemicals were analytical grade or better.
3.2.2 Grain and flour physicochemical properties
Kernel characteristics
About two hundred intact kernels from each sample were collected after removing broken, non-uniform kernels and foreign materials. Weight, moisture content, hardness, and diameter of each individual kernel were determined using a Perten 4100 Single Kernel
Characterization System (SKCS) (SKCS 4100, Perten Instruments, Inc., Springfield, IL)
(AACCI approved method 55-31.01, 2010). All parameters were reported on a dry weight basis.
GPC determination
Protein content of grains was determined by near infrared spectroscopy (Infratec 1241,
FOSS NIR Systems Inc., Denmark) and reported on a 12 % moisture basis (AACCI approved method 39-11.01, 2010).
52
3.2.3 Polymeric proteins analysis
Polymeric proteins are a group of macromolecules consisting of different glutenin subunits (GS) linked through intermolecular disulfide bonds (Southan and MacRitchie,
1999; Delcour et al., 2012). Experiments were conducted according to a two-step extraction procedure to determine the relative amount and size distribution of glutenins in each sample (Gupta et al., 1993; Johansson et al., 2001; Kuktaitea et al., 2004). All SDS- extractable proteins are extracted in the first step, followed by a second step extracting
SDS-unextractable proteins. For the first extraction, 0.01 g flour was suspended in 1mL extracting solution (0.5% SDS, 0.05M sodium phosphate buffer, pH 6.9) and vortexed for
5 s. The suspension was then centrifuged at 8,800xg for 40 min on an Eppendorf 5413 centrifuge (Eppendorf, Hauppauge, NY). The resulting supernatant was collected and filtered through a 0.45 μm filter paper (Pall Corporation, Ann Arbor, MI) prior to analysis.
For the second extraction, the pellet was resuspended in 1 mL protein-free extracting solution and vortexed for 5 s, followed by 30 s sonication at 5 W (Model 100 Sonic
Dismembrator, Fisher Scientific, Pittsburgh, PA) to dissolve SDS-unextractable proteins.
Centrifugation was then repeated at 8,800xg for 40 min and the resulting supernatant was collected and filtered. Extractions from the two extraction steps were fractionated through
SE-HPLC (Waters 2695, Waters Corporation, Milford, MA) using old and new model
PhenomenexBioSep SEC-S 4000 columns as detailed in the introduction (Phenomenex,
Torrance, CA) and a guard column (KJ0-4280, Phenomenex, Torrance, CA). 10 μL filtered supernatants were injected and eluted with 50% (v/v) acetonitrile and water contain 0.1% (v/v) TFA at flow rate of 0.45 mL.min-1 for 15 min. Samples were detected 53 at 214 nm using a Waters 2996 photodiode array detector (Waters Corporation, Milford,
MA).
The chromatograms of extractions were separated into four parts with decreasing protein molecular weight (Figure 3.1): large polymeric proteins (LPP), small polymeric proteins
(SPP), large monomeric proteins (LMP) and small monomeric proteins (SMP) by peak integration at times for LPP at the time where chromatogram trace leaves the baseline to
5.60 min; SPP from 5.60 to 6.75 min; LMP from 6.75 to 8.5 min; SMP was not defined
(peak C in Figure 3.1) (Johansson et al., 2001; Kuktaite et al., 2000). The percentage of large unextractable polymeric protein (% LUPP) and total unextractable polymeric protein (% TUPP) were calculated as below:
퐿푃푃 % 퐿푈푃푃 = 푢푛푒푥푡푟푎푐푡푎푏푙푒 × 100 퐿푃푃푒푥푡푟푎푐푡푎푏푙푒 + 퐿푃푃푢푛푒푥푡푟푎푐푡푎푏푙푒
퐿푃푃 + 푆푃푃 % 푇푈푃푃 = 푢푛푒푥푡푟푎푐푡푎푏푙푒 푢푛푒푥푡푟푎푐푡푎푏푙푒 퐿푃푃푒푥푡푟푎푐푡푎푏푙푒 + 푆푃푃푒푥푡푟푎푐푡푎푏푙푒 + 퐿푃푃푢푛푒푥푡푟푎푐푡푎푏푙푒 + 푆푃푃푢푛푒푥푡푟푎푐푡푎푏푙푒
× 100
54
To develop the peak integration for the new column, the chromatogram was adjusted by matching the ratios of the peak areas with old column. The ratio was calculated and matched on the basis of the following two equations:
Area of LPP old / Area of SPP old = Area of LPP new / Area of SPP new
Area of LPP old / Area of (LPP + SPP) old = Area of LPP new / Area of (LPP + SPP) new
The new integration was verified by correlation analysis between old and new column results for the same sample set, and also confirmed by mixograph dough properties.
55 a1
B
A
C
LPP SPP LMP SMP
b1
A B C
LPP SPP LMP SMP
a2
B’
A’
C’
LPP SPP LMP SMP
b2
A’ C’ B’
LPP SPP LMP SMP
Figure 3.1: Representative SE-HPLC chromatograms of old (a1 and b1) and new (a2 and b2) columns (sample: Norwest 553, Condon, 2011). (a) First extraction: SDS-extractable proteins and (b) Second extraction SDS-unextractable proteins. Both chromatograms were separated into four parts including LPP, SPP, LMP and SMP. A and A’ represent the sum of LPP and SPP fractions, B and B’ represent the LMP fraction as well as C and C’ represent the SMP fraction. 56
3.2.4 Mixograph method
Dough mixing properties were evaluated using the Mixograph method (National
Manufacturing, Lincoln, NE) according to AACCI approved method 54-40.02 (AACC
International, 2010). Time to Mixograph peak was reported in minutes.
3.2.5 Pasting properties
Falling number (FN)
Flour paste properties under hydrothermal treatment were determined using a Falling
Number apparatus (FN 1700, Perten Instruments, Inc., Springfield, IL) according to
AACCI approved method 56-81.03 (AACC International, 2010). FN results were reported as seconds.
RVA analyses
To monitor hydrothermal processing, Rapid Visco Analyzer (RVA) (RVA 4500, Perten
Instruments, Inc., Springfield, IL) analyses were conducted in addition to FN. RVA analyses were used to visualize and evaluate flour paste properties using either controlled heating conditions (adapted from AACCI Approved Method 76-21.01, 2010) or “ballistic”
(fast) heating conditions using the RVA-based Stirring Number procedure (SN: AACCI approved method 22-08.01, 2010). RVA results were reported as viscosity in centipoise
(cP; 0.001 kg m-1 s-1). The ballistic heating conditions in the SN method (heating block set to 95℃) are analogous to those encountered by the sample in the FN where the sample is subjected to the test’s maximum temperature (for FN, 100℃ at sea level) immediately and sample temperature rises at the maximum possible rate (Figure 2.9). 57
Fast heating
Fast heating was performed to observe protein behavior at specified time intervals during the SN test. As a practical necessity demanded by the rapidity of heating, and in contrast with controlled heating conditions described below, samples were collected at specified time points rather than at specified temperatures. Samples of the heated flour pastes were collected at 10, 20, 30, 60, 120 and 180 s. For each time point a separate SN run was done and stopped at the specified time point for sampling. The reported parameter was viscosity (cP) at each time point.
Controlled heating
To improve the resolution of the fast heating conditions of the SN test, a slower temperature increase was applied. Flour pastes were collected at specified temperatures during heating so protein behaviors during hydrothermal treatment could be observed.
Straight-grade flour (4 g) was suspended in 25 mL deionized (DI) water and introduced to RVA. Temperature was increased from 20℃ to 80℃ at a 2℃ min-1 during the test. For each temperature point a separate controlled temperature run was done and stopped at the specified temperature point for sampling. The reported parameter was viscosity (cP) at each temperature point.
Sample preparation for SE-HPLC analyses
Samples collected at specified time or temperature intervals during RVA fast and controlled heating experiments respectively, were prepared for SE-HPLC analyses by the following method. After each run, flour pastes and controls were immediately frozen in 58 liquid nitrogen. Samples were subsequently freeze-dried (VirTis Consol 4.5, SP
Scientific, Warminster, PA). Freeze dried materials were ground manually using a mortar and pestle (Lagrain et al., 2005; Lagrain et al., 2008). The resulting freeze-dried powders were sieved (0.150 mm sieve opening) to achieve a similar particle size distribution as found in the original flour. Coarse material not passing 0.150 mm was reground and sieved until all freeze-dried powder passed the sieve. All the samples and controls were then assessed for LUPP% using SE-HPLC (section 3.2.3). For each sample, a flour/water mixture made by hand-mixing in 20℃ DI water, was used as control-2. Unprocessed (dry) flour was used as control-1. Onset temperature for heat setting was defined as a > 2% increase over the %LUPP value of control 2.
3.2.6 Particle size analysis
The particle size distribution of 40 mg whole-wheat flour suspended in 150mL isopropanol alcohol (IPA) was measured on a Mastersizer & Hydro 2000s (Malvern
Instruments, Ltd., Malvern, Worcestershire, UK). IPA was used instead of water to prevent flour particle swelling. Particle refractive parameter and absorption indices were set to 1.52 and 0.1 respectively (Hu et al., 2007). Obscuration varied from 7% to 12% indicated optimal sample concentrations. Laser diffraction was conducted based on the assumption that flour particles conform to a hard, spherical particle model (Rawle, 1993).
Laser PSA provides a number of calculated values (e.g., diameter mean, surface mean, weight mean, moment mean) and these were used to investigate the size distribution and properties of samples. For instance the parameters, d (0.1), d (0.5), and d (0.9) indicate whether 10%, 50% (i.e. median), or 90% of particles are below these diameters, 59 respectively. Sample size distribution can be well illustrated using those three values
(Treviranus, 2011). D [3, 2] and D [4, 3] are the surface weighted mean and volume weighted mean, respectively. These parameters describe the centers of gravity of the corresponding distributions and remove the number of particles being used in calculations (Rawle, 1993).
3.2.7 Statistical analyses
All the analyses were performed in duplicate, except for Mixograph and GPC, which were done in singlet. For all duplicated analyses the coefficient of variation (CV) was kept under 10%. If on the first duplication CV > 10%, repeat duplicated runs were done until CV was less than 10%. For particle size determination, a control was included every day of testing. Pairwise correlations were applied to evaluate relationships between different effects, and the significance for “r” was set at P ≤ 0.01. One-way and multifactor ANOVAs were used to determine the significance of each main effect at P ≤
0.01. Statistical analyses were performed using JMP 11 (SAS Institute Inc., Cary, NC).
3.3 Results and Discussion
3.3.1 New SE-HPLC column validation
Chromatograms from both the old and new columns of a representative sample of
Norwest 553 (2011, Condon) are shown in Figure 3.1. Upon testing SDS-extractable proteins, the new column shortened peaks A and B (to A’ and B’ resp.) but improved the resolution of peak B, since a cleaner peak, without the shoulder seen at 9 min elution time, 60 was observed. Moreover, the shift of the minimum left of peak C at 9.2 min to the right of peak C’ beginning at 10.5 min was preferable in data analysis since the problematic solvent front was now separated from the regions of interest. The negative minimum prior to peak C may be a solvent front that elutes prior to the SMP, water-soluble, albumins in the old model column. New packing increased the precision of peak A
(HMW-glutenin fraction) because a much clearer separation was observed between peaks
A’ and B’. These are more important than the other peaks in this experiment (Shewry et al., 1992). Similar changes were observed on the chromatograms of the sonicated SDS- unextractable proteins obtained from the two columns (Figure 3.1 b).
The lowest point (at 5.55 min) between peak A’ and B’ in new column chromatogram
(Figure 3.1) was considered to be the separation point between polymeric proteins and monomeric proteins. In order to separate LPP and SPP, peak A’ (new column) was divided to match the LPP/SPP area ratio of peak A (old column: section 3.2.3). The middle of the plateau (at 7.80 min) after peak B’ was selected as the division between
LMP (gliadins) and SMP (albumins and globulins). In summary, 4.85, 5.55 and 7.80 min were used as cutoff points for integration of new column.
Correlation analysis between old and new columns showed a significant %LUPP relationship (r = 0.93, P ≤ 0.01) (Figure 3.2). This suggests that the calculated integration of new column was comparable with old column. To further demonstrate the accuracy of new integration, the same sample set was assessed for dough mixing characteristics on a
Mixograph apparatus. Representative good and poor quality Mixograms are shown in 61
Figure 3.3. Samples with the highest %LUPP had Mixograms similar to that shown in
Figure 3.3a, whereas the lowest %LUPP samples had poor Mixograms similar to that shown in Figure 3.3b. It is known that samples with high polymeric protein have longer mixing times and better mixing tolerance as reflected in the Mixograms (Carson and
Edwards, 2009). The correlation between mixograph peak time and %LUPP from the new column for this sample set showed a positive relationship (r = 0.83, P ≤ 0.01)
(Figure 3.4a). In the old column, a significant but more scattered correlation between %LUPP and time to peak (r = 0.66, P ≤ 0.01) was observed (Figure 3.4b).
Therefore, for future %LUPP analysis, the integration strategies applied here for the new
SE-HPLC column are appropriate. The new column seems more accurate than old column model in %LUPP analysis since it had a higher correlation with mixograph peak time.
80 70 R² = 0.8657 60 50 40 30
Old Column (%) Column Old 20 10 0 0 20 40 60 80
New Column (%)
Figure 3.2: Correlation analysis between old and new column %LUPP responses based on the same sample set.
62 a b
Figure 3.3: Typical good (a) and poor (b) quality mixograph results.
a 8 b 8 7 7 6 R² = 0.8304 6 R² = 0.4395
5 (min) 5
4 4 Peak 3 3 2 2 1
1 Mixograph
Mixograph Peak (min) Peak Mixograph 0 0 0 20 40 60 80 0 20 40 60 80 LUPP (%) LUPP (%)
Figure 3.4: Correlation analyses between mixograph peak time in minute versus new (a) and old (b) column %LUPP. 63
3.3.2 Thermal stability of gluten proteins in a heated flour-water system
Table 3.1 shows grain protein content, FN and hardness of the chosen samples.
Norwest 553 from Condon and ORSS-1757 from Lexington were grouped to observe changes in one dimension: the same GPC but different gluten compositions and dough properties (Ross Pers. Comm). Samples of Norwest 553 from Corvallis and LaGrande were grouped to observe the behavior of a single variety with strong dough properties across a range of GPC. Similarly, samples of ORSS-1757 from Corvallis and Lexington were also are grouped to observe the behavior of a single variety with weak dough properties across a range of GPC.
Sample moisture contents were collected as references for the tempering process (data not shown). GPC had a positive relationship with FN on the basis of selected samples (r
= 0.93, P ≤ 0.01) in accordance with the results of Ross et al. (2012).
Based on two-way ANOVA there was no significant difference in %LUPP between the hand-mixed flour/water suspension (control 2) and the unprocessed flour (control 1)
(Table 3.2). However, the P value (P = 0.027) is between 0.01 and 0.05, which indicates the freeze-drying process may have slightly impacted flour properties, with about 0.8 to
3.7% loss of polymeric proteins associated with the hydration and freeze drying process used to create control 2 (Table 3.1).
64
Table 3.1: Grain protein content, falling number and hardness of selected samples
Hardness Freeze-dried Flour GPC FN Variety Location (Hardness control control (%)*** (sec)** Index)* (%LUPP)** (%LUPP)** Norwest 553 Condon 11.8 535 ± 6 67.8 ± 16.8 63.0 ± 0.3 63.8 ± 1.6 ORSS-1757 Lexington 11.7 480 ± 11 16.2 ± 14.2 43.7 ± 1.4 47.4 ± 3.1
Norwest 553 Corvallis 9.6 445 ± 8 58.3 ± 18.6 62.8 ± 1.7 64.7 ± 4.2 Norwest 553 LaGrande 14.0 552 ± 16 74.9 ± 16.2 59.2 ± 0.1 60.9 ± 1.3
ORSS-1757 Corvallis 8.4 419 ± 13 4.4 ± 15.9 38.4 ± 1.0 39.1 ± 0.2 ORSS-1757 Lexington 11.7 480 ± 11 16.2 ± 14.2 43.7 ± 1.4 47.4 ± 3.1
* Standard deviation of 200 individual kernels. ** Data were reported as mean value ± standard deviation. *** GPC was single measured: NIR protein repeatability ± 0.5% (Osborne and Fearn, 1983).
Table 3.2: Two-way ANOVA results of control effect. Source DF Sum of Squares F Ratio Prob > F Sample 4 1048.7 388.5 < 0.0001 Control 1 7.8 11.5 0.027
* DF = degrees of freedom.
65
Fast heating
Fast heating uses the standard stirring number test. In this case 95℃ is reached in 30 s and the temperature is kept at 95℃ until the end of the test at 3 min (Figure 2.9). Figure
3.5 shows that %LUPP of all the samples increased when temperature increased. This is because heating triggers unfolding and heat-setting of gluten proteins. The cross-linking and aggregation of polypeptides reduces protein extractability thereby causes an increase in %LUPP values (Lagrain et al., 2005). For samples with same GPC but different gluten compositions (Figure 3.5a), ORSS-1757 appeared to have a marginally faster increase of %LUPP than Norwest 553 and both varieties arrived at the same %LUPP at the end of the 3 min. For Norwest 553 with strong dough properties and ORSS-1757 with weak dough properties across a range of GPC (Figures 3.5b and 3.5c), the rates of %LUPP increase were similar.
In FN test, since the temperature increase is extremely fast, protein heatset has already finished during the initial 60 sec mixing stage. Similarly, in the SN test temperature is very likely to have passed the threshold temperature for onset of heat-setting before the first sampling point at 10 s. It is evident from Figure 3.5 that there has already been an increase in %LUPP between the control (y-axis) and the first sampling point. Therefore, the fast heating process was too fast to observe the onset of protein heat-setting. To increase the resolution, a controlled heating test was performed (see “controlled heating” below). The major difference between two heating rate tests was the absence of the step change seen during controlled heating (Figure 3.6). 66
Norwest 553 a. Fast Heating - Same GPC ORSS-1757 100 90 80 70
LUPP (%) LUPP 60 50 40 30 0 50 100 150 200 Time (s)
GPC=14.0 b. Fast Heating - Norwest 553 GPC=9.6 100 90 80 70 60
LUPP (%) LUPP 50 40 30 0 50 100 150 200 Time (s)
GPC=11.7 c. Fast Heating - ORSS-1757 GPC=8.4 100 90 80 70 60 LUPP (%) LUPP 50 40 30 0 50 100 150 200 Time (s)
Figure 3.5: Results of fasting heating experiments. (a) Same GPC in both hard and soft wheat varieties (GPC = 11.8% in Norwest 553 & GPC = 11.7% in ORSS-1757), (b) different GPC in hard wheat variety Norwest 553 (GPC = 14.0% & 9.6%) and (c) different GPC in soft wheat variety ORSS-1757 (GPC = 11.7% & 8.4%). Data collected at 0 s was control 2. 67
Controlled heating
Compared with fast heating that used the standard SN test, in which 95℃ was reached in
30 s (Figure 2.9), controlled heating observed gluten protein behavior from 20 to 80℃ at
2℃ per minute. This allowed us to obtain the heat-setting onset temperature during the flour hydrothermal processing.
Figure 3.6 shows that %LUPP of all the samples increased when temperature increased past a threshold. This is because heating beyond the threshold temperature triggers unfolding and heat-setting of gluten proteins. The cross-linking and aggregation of polypeptides reduces protein extractability thereby causes an increase in %LUPP values
(Lagrain et al., 2005). Heat-setting onset temperature was considered to be a > 2% increase of the %LUPP value over the freeze-dried flour control (control 2: Table 3.3).
For samples with same GPC but different gluten compositions, ORSS-1757 had less %LUPP at all temperatures than Norwest 553 (Figure 3.6a). Since the FN of ORSS-
1757 was lower than Norwest 553 (Table 3.1), we can conclude that %LUPP is positively associated with FN for the group with constant GPC. However, ORSS-1757 had a faster increase in %LUPP than Norwest 553 (Figure 3.6a). A lower heat-setting onset temperature was also observed for ORSS-1757 compared to Norwest 553 (Table 3.3).
This suggests that samples with shorter gluten polypeptides (ORSS-1757) heatset faster at lower onset temperatures than longer ones (Norwest 553), which is contrary to our hypothesis and also contrary to the findings of Kovacs et al. (2004).
68
For the variety Norwest 553 across a range of GPC, the control %LUPP values were very close between samples. Sample with lower GPC showed lower FN (Table 3.1). This suggests that for the group where GPC varied widely, it is the GPC, rather than %LUPP plays the dominant role in modulating FN. Similar trends of %LUPP increase versus temperature were observed for the Norwest 553 samples at either 9.6 or 14.0% GPC
(Figure 3.6b). The heat-setting onset temperatures also appeared to be very close at both
GPC levels (Table 3.3). This indicated that GPC in this strong dough variety had no detectable effect on gluten heat-setting properties but, as noted above, GPC was positively associated with FN.
For the variety ORSS-1757 across a range of GPC, the sample with lower GPC possessed lower levels of %LUPP and had lower FN (Table 3.1). This suggests that for the ORSS-
1757 pair where GPC varied widely, besides GPC, %LUPP also may have played a role in modulating FN. %LUPP increase at low GPC was faster than at high GPC (Figure
3.6c). A lower heat-setting onset temperature was also observed for ORSS-1757 with lower level of GPC (Table 3.3). Therefore, GPC in this weak dough variety had some effect on heatset rate and the onset temperature, with a faster heatset rate and lower onset temperature in low GPC samples.
Several things can be concluded according to above results: our hypothesis “longer gluten polypeptides are more flexible and would therefore heatset faster than shorter ones” cannot be demonstrated. Results were opposite, with shorter gluten peptides heat-setting faster. GPC had no effect on strong dough heatset properties, but only affected weak 69 dough heatset properties. Gluten protein composition and GPC both play an important role in protein heatset behaviors. For the group with constant GPC, %LUPP is positively correlated to FN. For the group where GPC varied widely, both GPC and %LUPP play an important role in FN, with positive associations between FN versus GPC and %LUPP.
Overall, the flour-water system is very complex and each variable might not be independent. Presumably, there are other confounding factors related to protein properties that affect starch granule behaviors, e.g. protein clumping may occur during the thermal treatment. Using our current techniques by obtaining relative amount of gluten proteins
(%LUPP) during the initial heating process to evaluate flour-water system at a molecular level is difficult. Future work includes the extraction of starch, followed by observing starch and protein behaviors separately in model systems.
Table 3.3: Onset temperatures under controlled heating. GPC Onset temperature for heat-setting Variety Location (%) (℃)* Norwest 553 Condon 11.8 63 ± 1 ORSS-1757 Lexington 11.7 54 ± 2 Norwest 553 Corvallis 9.6 59 ± 1 Norwest 553 LaGrande 14.0 60 ± 2 ORSS-1757 Corvallis 8.4 50 ± 0 ORSS-1757 Lexington 11.7 54 ± 2 * Onset temperature for heat-setting was defined as a > 2% increase over freeze-dried control of the %LUPP value. Data were reported as mean value ± standard error. 70
a. Controlled Heating Norwest 553 - Same GPC ORSS-1757 80
70
60
LUPP (%) LUPP 50
40
30 20 30 40 50 60 70 80 90 Temperature (°C)
b. Controlled Heating GPC=14.0 - Norwest 553 GPC=9.6 80
70
60
50 LUPP (%) LUPP 40
30 20 30 40 50 60 70 80 90 Temperature (℃)
c. Controlled Heating GPC=11.7 - ORSS-1757 GPC=8.4 80
70
60
50 LUPP (%) LUPP 40
30 20 30 40 50 60 70 80 90 Temperature (℃)
Figure 3.6: Results of controlled heating experiments. (a) Same GPC in both hard and soft wheat varieties (GPC = 11.8% in Norwest 553 & GPC = 11.7% in ORSS-1757), (b) different GPC in hard wheat variety Norwest 553 (GPC = 14.0% & 9.6%) and (c) different GPC in soft wheat variety ORSS-1757 (GPC = 11.7% & 8.4%). Data collected at 20 ℃ was control 2. 71
3.3.3 Relationship between flour particle size and falling number
There was a significant correlation between kernel hardness and FN (r = 0.50, P ≤ 0.01)
(Figure 3.7). Thus, as mentioned in hypothesis 2, we suggested that larger flour particles obtained from harder kernels were expected to delay starch swelling, subsequent breakdown and result in increased FN. In order to investigate the relationship between flour particle size and FN, correlation analyses were conducted on selected samples
(Appendix 1). Originally, we intended to use sieving analysis (data not show) to evaluate particle size differences between samples. However, the reproducibility of data was unsatisfactory. Therefore, we decided to use a laser particle size analyzer for flour particle size analysis (PSA).
Table 3.4 shows mean FN and calculated mean values of the five PSA parameters. The correlation between FN and particle size (in terms of d and D values) was insignificant in most cases, especially Bobtail and Brundage 96. Only the FN of Coda was correlated with its d (0.9) (r = 0.86, P ≤ 0.01) and D [4, 3] (r = 0.86, P ≤ 0.01). This was further demonstrated by analysis of 50 randomly selected samples (Table 3.5), where no correlations were found between mean FN and those five PSA parameters.
In summary, particle size had no significant impact on FN when the flour was milled through a hammer mill with a 0.8 mm screen. Suggesting that the screen masked potential differences in particle size related to kernel hardness. This is contrary to our hypothesis. More specifically, it appears that starch swelling during heat treatment was not affected by initial flour particle size in this sample set where kernel hardness index 72 varied from 9.3 to 51.4. A delayed hydration in larger particles might exist but will not result in detectable differences in starch pasting properties, since granule breaking could be an extremely fast process regardless of particle granularity (Ross Pers. Comm). This suggested that kernel hardness did not affect FN as differentiated by initial flour particle size. Since hard wheats are commonly selected for higher dough strength with more protein content compared with soft wheats, it probably because of the protein rather than hardness itself, caused the effects on FN.
60
50 R² = 0.2534
40
30
20
10 Hardness (Hardness Index) (Hardness Hardness
0 0 100 200 300 400 500 600 FN (sec)
Figure 3.7: Correlation analysis between mean FN (sec) and kernel hardness (results obtained from SKCS). 73
Table 3.4: Correlations between mean FN and wheat particle size of three selected varieties.
Variety Parameter r P Bobtail d (0.1) 0.06 0.82 d (0.5) 0.30 0.26 d (0.9) 0.43 0.10 D [3, 2] 0.21 0.44 D [4, 3] 0.44 0.09 Coda d (0.1) 0.10 0.82 d (0.5) 0.45 0.26 d (0.9) 0.86 0.006* D [3, 2] 0.05 0.90 D [4, 3] 0.86 0.006*
Brundage 96 d (0.1) 0.30 0.28
d (0.5) 0.27 0.34
d (0.9) 0.17 0.55
D [3, 2] 0.26 0.36
D [4, 3] 0.18 0.51 * Data showed significant correlation at P ≤ 0.01.
Table 3.5: Correlations between mean FN and wheat particle size of 50 randomly selected samples.
Parameter r P d (0.1) 0.12 0.41 d (0.5) 0.00 0.99 d (0.9) 0.05 0.74 D [3, 2] 0.04 0.79 D [4, 3] 0.04 0.78 74
3.4 Conclusions
New integration was given at 4.85, 5.55 and 7.80 min for SE-HPLC method development with BioSep-SEC-S 4000 silica column (Phenomenex) in evaluation of wheat polymer proteins.
Gluten protein composition and GPC both play an important role in protein heatset behaviors, and served as non-amylase modulators of FN. However, our hypothesis
“longer gluten polypeptides are more flexible and would therefore heatset faster than shorter ones” cannot be demonstrated. In contrast, our results showed that samples with lower MWD had faster heatset times than samples with higher MWD according to a controlled heating test. The role of wheat protein during starch granule swelling remains unclear at this point and requires further investigation. No correlations have been found between FN and initial flour particle size.
This work will be followed by: measuring the FN in reducing agent that stops all disulfide bonding. We can also use other techniques (e.g., FTIR) to observe protein aggregation behavior by detecting the formation/reduction of disulfide linkage. In order to reduce the complexity of the flour-water system and eliminate the potential interference, future work includes the extraction of starch, followed by observing starch and protein behaviors separately in model systems.
75
References
AACC International. 2010. Approved Methods of Analysis, 11th Ed. Methods 22-08.01, 39-11.01, 54-40.02, 55-31.01, 56-81.03. Available online only. AACCI: St. Paul, MN.
Carson, G. R., Edwards, N. M. 2009. Criteria of wheat and flour quality. In: Khan, K., Shewry, P.R. (Eds.), Wheat: Chemistry and technology. AACC International, Inc., St. Paul, MN, pp. 97-118.
Craven, M., Barnard, A., Otto, W., and Labuschagne, M. T. 2007. Classification of South African bread wheat cultivars according to Hagberg falling number reaction to fertilizer treatment. Cereal Chem. 84:214- 219.
Delcour, J. A., Joye, I. J., Pareyt, B., Wilderjans, E., Brijs, K., Lagrain, B. 2012.Wheat gluten functionality as a quality determinant in cereal-based food products. Annu. Rev. Food Sci. Technol. 3:469-492.
Flowers, M., Peterson, C. J., Burns, J., Kuehner, J. 2008. Norwest 553. Hard red winter wheat. EM 8967-E. New wheat varieties. Oregon State University Extension Service.
Flowers, M., Peterson, C. J. 2010. ORSS-1757. “SuperSoft” Soft white winter wheat. EM 9016. New wheat varieties. Oregon State University Extension Service.
Goesaert, H., Gebruers, K., Courtin C. M., Brijs, K., Decour, J. A. 2006. Enzymes in breadmaking. In: Hui, Y. H., Corke, H., De Leyn, I., Cross, N. (Eds.), Bakery products: science and technology. Blackwell Publishing, Ames, IA. pp. 337-364.
Gupta, R.B., Khan, K., Macritchie, F., 1993. Biochemical Basis of Flour Properties in Bread Wheats .1. Effects of Variation in the Quantity and Size Distribution of Polymeric Protein. J. Cereal Sci. 18, 23-41.
Hayta, M., Schofield, J. D. 2004. Heat and additive induced biochemical transitions in gluten from good and poor breadmaking quality wheats. J Cereal Sci. 40:245-256.
Hoseney, R. C., Zeleznak, K., Lai, C. S. 1986. Wheat gluten: A glassy polymer. Cereal Chem. 63:285-286.
Hu, Y., Liu, Z., Cen, T., Sun H. 2007. The research on the analysis method of the wheat flour’s particle size distribution by laser diffraction. Journal of Henan University of Technology (Natural Science Edition). 28:22-32.
Jeffers, H. C., Rubenthaler, G. L. 1977. Effect of roll temperature on flour yield with the Brabender Quadrumat experimental mills. Cereal Chem. 54:1018 - 1025.
76
Johansson, E., Prieto-Linde, M.L., Jonsson, J.O., 2001. Effects of wheat cultivar and nitrogen application on storage protein composition and breadmaking quality. Cereal Chem. 78:19–25.
Kuktaite, R., Johansson, E., Juodeikiene, G., 2000. Composition and concentration of proteins in Lithuanian wheat cultivars: relationships with bread-making quality. Cereal Res. Communications 28:195 – 202.
Kuktaite, .R, Larssonb H., Johansson E. 2004. Variation in protein composition of wheat flour and its relationship to dough mixing behavior. J. Cereal Sci. 40:31–39.
Lagrain B., Brijs K., Veraverbeke W. S., Delcour J. A. 2005. The impact of heating and cooling on the physico-chemical properties of wheat gluten-water suspensions. J. Cereal Sci. 42:327–33.
Lagrain B., Thewissen B. G., Brijs K., Delcour J. A. 2008. Mechanism of gliadin- glutenin cross-linking during hydrothermal treatment. Food Chem. 107:753–60.
Mansour, K. 1993. Sprout damage in wheat and its effect on wheat flour products. In: Walkin-Simmons M. K., Ried, J. L. (Eds.), Pre-harvest sprouting in cereals. AACC International, Inc., St. Paul, MN, pp. 8-9.
McFall, K. L., Fowler, M. E. 2009. Overview of wheat classification and trade. In: Carver, B.F. (Ed.), Wheat Science and Trade. Wiley-Blackwell, Ames, Iowa, pp. 439-454.
Noel, T. R., Parker, R., Ring, S. G., Tatham, A. S. 1995. The glass-transition behaviour of wheat gluten proteins. Int. J. biol Macromol. 17:81-85.
Osborne, B. G., Fearn, T. 1983. Collaborative evaluation of near-infrade reflectance analysis for the determination of protein, moisture and hardness in wheat. Journal of the Science of Food and Agriculture 34:1011-1017.
Perten, H. 1964. Application of the falling number method for evaluating alpha-amylase activity. Cereal Chem. 41:127-140.
Rawle, A. 1993. Basic principles of particle size analysis. Malvern instrument technical paper MRK034. Malvern Instruments Limited, Enigma Business Park, Grovewood Road, Malvern, Worcestershire, WR14 1XZ, UK.
Ross, A. S., Bettge, A. D. 2009. Passing the test on wheat end-use quality. In: Carver, B.F. (Ed.), Wheat Science and Trade. Wiley-Blackwell, Ames, Iowa, pp. 455-486.
Ross, A., Flowers, M., Zemetra, R., Kongraksawech, T. 2012. Effect of grain protein concentration on falling number of ungerminated soft white winter wheat. Cereal Chem. 89:307-310. 77
Schofield, J. D., Bottomley, R. C., Timms, M. F., Booth, M. R. 1983. The effect of heat on wheat gluten and the involvement of sulphydryl-disulphide interchange reactions. J. Cereal Sci. 1:241-253.
Shewry. P. R., Halford N. G., Tatham A. S. 1992. High-molecular-weight subunits of wheat glutenin. J. Cereal. Sci. 15:105-120.
Southan, M., MacRitchie, F., 1999. Molecular weight distribution of wheat proteins. Cereal Chem. 76:827-836.
Treviranus, I. 2011. Interpreting your laser diffraction particle size analysis results (Decoding the acronyms and finding insights). HORIBA Particle slide share. Access online.
Zhang, Y., He, Z. H., Ye, G. Y., Aimin, Z., and Van Ginkel, M. 2004. Effect of environment and genotype on bread-making quality of spring-sown spring wheat cultivars in China. Euphytica. 139:75-83.
Zhang, Y., He, Z. H., and Ye, G. Y. 2005. Milling quality and protein properties of autumn-sown Chinese wheats evaluated through multi-location trials. Euphytica. 143:209-222.
78
Chapter 4: Investigation of Gliadin Proteins in Wheat Varieties Dating From 1800 to 2011
Ying Zhang, Colleen Roseborough, Teepakorn Kongraksawech, Jae Bom Ohm, Robert S. Zemetra, and Andrew S. Ross
Abstract
Wheat is implicated in celiac disease (CD) and non-celiac gluten sensitivity (NCGS).
Recently, the incidence of CD has increased in both adults and children. Since gliadins trigger CD, it is worth investigating whether there have been any changes in their composition over time. We chose an alternative, coarse-grained, way to investigate potential changes in gliadins that may lead to improved targeting of fine-grained analyses of potential allele-level changes. Sixty-two soft and 61 hard U.S. high production wheat varieties from 1900 to the present (with one from 1800) were collected and analyzed by
RP-HPLC. These varieties were investigated to begin to answer whether wheat breeding for higher dough strength, or the incorporation of dwarfing alleles after the 1960s, was associated with observable changes in gliadin composition. ANOVA results suggested that deliberate breeding for dough strength, as illustrated by the hard versus soft wheat contrast, had not systematically changed the relative abundance of α/β-gliadins across the last 110 years, but had altered the relative abundance of the other two fractions. ANOVA results indicated no change in proportions of the three gliadin fractions after deployment of the dwarfing alleles suggesting the tall to dwarf change was independent of gluten composition. Second order polynomial regression analyses showed that the relative abundance of α/β-gliadins increased until around 1960 then decreased. The changes were more noticable in the hard wheats. The converse was observed for 훾-gliadins. This 79 stepwise change questioned the association between CD increase and breeding for increased dough strength in hard wheats, since the relative abundance of α/β-gliadins did not keep going up, and α-gliadin is considered the major trigger force for CD initiation.
In contrast, linear correlation analyses with each of 700, three second long fractions of the RP-HPLC chromatograms suggested that most changes were related to the soft wheat population. The discrepancy between the regression analyses of the three major fractions and the 700 small fractions may be related to the use of linear correlations in the latter when some relationships were clearly non-linear. Overall, our results did not fully support speculations that there have been profound changes in gluten composition related to the dwarfing alleles or selection for increased dough strength in hard wheats.
4.1 Introduction
Wheat has been selected for desirable traits for centuries. Replanting of the selected seeds year by year increases the frequency of specific alleles. For example hard wheats, used for dough-based products like breads, have undergone selection for higher dough strength.
Wheat gluten proteins, which are made up of gliadins and glutenins, contribute to the unique wheat dough properties. Hydrated gliadins are sticky and contribute to dough viscosity (flow), while glutenins are resilient and account for dough elasticity (Delcour and Hoseney, 2010). Another selected trait is plant height. Genetically “wild-type” wheat
(Rht-B1a and Rht-D1a) is a tall plant (> 120 cm). Long stalks easily bend from the weight of the spike during grain filling, resulting in lodging and loss of yield. A significant change occurred after 1960 when O.A. Vogel crossed Japanese dwarf wheat
“Norin 10” with the lodging resistant wheat “Brevor”, incorporating the dwarfing alleles 80
(Rht-D1b and Rht-B1b) into U.S. wheats, and created high yielding wheat cultivars < 100 cm tall (Vogel et al., 1963; Ram, 2014). The first commercial semi-dwarf wheat variety was Gaines, a soft white wheat released in 1961. This was the beginning of planting high yielding short wheats and known as the “Green Revolution”.
Wheat has been implicated, with or without evidence, in some human pathologies. The most recognized evidence-based pathology is celiac disease (CD), an immune-mediated gut disorder initiated by the ingestion of the gluten-containing grains wheat, barley, rye and possibly oats (Fasano et al., 2003; Wieser and Koehler, 2008; Shewry, 2009; Sapone et al., 2012). Clinical symptoms of CD include abdominal pain, anemia, weight loss, failure to thrive, vomiting, diarrhea, etc. with continued gluten consumption (Tonutti and
Bizzaro, 2014). It has been reported that the prevalence of CD is 0.71% in the US and similar numbers are found in European countries (Rubio-Tapia et al., 2009; Mustalahti et al., 2010). CD has been demonstrated to be strongly associated with human leukocyte antigen (HLA) DQ2 and DQ8 (MHC class II). HLA-DQ2 antigen is found in approximately 95% of CD patients, the rest are HLS-DQ8 positive. About 10% of CD patients express both HLA-DQ2 and -DQ8 antigens (Karell et al., 2003).
Gliadins are a heterogeneous mixture of monomeric proteins consisting of α, β, γ and ω types. Ω-gliadins lack cysteine residues and therefore no inter- or intra-molecular disulfide bond formation occurs in ω-gliadins (Hsia and Anderson, 2013). Since the cysteine residues in α-/β- and γ-gliadins are located at highly conserved positions within the peptide structure, they do not participate in inter-molecular disulfide bond mediated 81 polymerization reactions (section 2.6.2: hence “monomeric”). Instead, α-/β- and γ- gliadins only exhibit intra-molecular disulfide bonds under ambient conditions (Delcour et al., 2012; Bonomi et al., 2013). In contrast, both intra- and inter-molecular disulfide bonds exist in polymeric glutenins. In glutenins the cysteine residues that engage in intra- or inter-molecular disulfide bond formation are located on the ends of the subunits
(Delcour et al., 2012).
CD is triggered by specific regions of α-gliadins. Many studies have focused on a 33-mer immunogenic peptide located at the N-terminal, amino-acid number 57-89 region (Shan et al., 2002). This 33-mer can be digested by intestinal enzymes but is resistant to further degradation by brush border enzymes, and becomes a good substrate for tissue transglutaminase (tTG) (Shan et al., 2002; Mowat, 2003; Tonutti and Bizzaro, 2014). tTG catalyzes deamidation, converting glutamine to glutamate residues. This results in a tight connection between the deamidated gliadin peptide and HLA-DQ2 or -DQ8 complexed with antigen presenting cells (APC) (Dewar et al., 2004; Meresse et al., 2009; Tonutti and
Bizzaro, 2014). Meanwhile, CD4+ T cells are activated after peptide recognition by T cell receptors (TCR) (Wieser et al., 2012). This T cell stimulation causes immune responses and secretes the inflammatory cytokines, which result in villus atrophy (Th-1 response). In addition, activated T cells facilitate the maturation of B cells that are responsible for production of IgA and IgG antibodies (Th-2 response) (Dieterich et al.,
1997; Fleckenstein et al., 2004; Wieser et al., 2012). These antibodies are used in particular for serological tests in CD diagnosis. CD diagnosis is commonly conducted by detection of IgA anti-tTG and anti-endomysial antibodies (specific antibody for gluten). 82
CD can also be assessed by biopsy that directly observes the varying degrees of villus atrophy (Tonutti and Bizzaro, 2014).
Recently, another gluten-associated disorder has been suggested where patients have negative CD test results, exhibit no duodenal mucosa damage, but share some, but less severe, CD symptoms like abdominal pain, anemia, headache, fatigue, diarrhea, etc.
(Sapone et al., 2012; Brouns et al., 2013; Khamsi, 2014). This disorder is termed “non- celiac gluten sensitivity” (NCGS). There is currently no clinical method of diagnosing
NCGS. It can only be distinguished by determination that the patient does not have either
IgE-mediated wheat allergy or CD, in parallel with the observation of gluten-induced symptoms and the potential efficacy of a gluten-free diet (GFD) (Sapone et al., 2012;
Tonutti and Bizzaro, 2014). There is controversy. Some researchers claim that an increasing number of people are eliminating gluten from their diets without adequate cause and some are also casting doubt on whether NCGS even exists (Biesiekierski et al.,
2011; Carroccio et al., 2012; Khamsi, 2014). Based on double-blind placebo-controlled surveys, it is still under debate whether “NCGS” is associated with gluten or other wheat components (e.g. fermentable carbohydrates known as fermentable oligo-, di-, and monosaccharides and polyols: FODMAPs, or amylase trypsin inhibitors) (Barrett and
Gibson, 2012; Junker et al., 2012; Biesiekierski et al., 2013). Similarly, NCGS is shown to be associated with irritable bowel syndrome (IBS) that is triggered by FODMAPs
(Verdu et al., 2009; El-Salhy et al., 2014).
83
Based on evidence that suggests that the incidence of CD has increased (Rubio-Tapas et al. 2009) wheat researchers have been interested to see if there have been changes in gluten composition in the modern era. Van den Broeck et al. (2010) suggested that the increase in CD may have come from wheat breeding that inadvertently or otherwise presented a greater frequency of CD epitopes (e.g. Gliadin-α9) as a side effect of breeding for increased dough strength. Others, not wheat researchers, have also made inferences about gluten in the modern era, which have gained popular followings. Willam
Davis (2011) in the sensationalist book “Wheat Belly” speculated that the introduction of dwarfing alleles caused a change in wheat gluten proteins that negatively affected human health. However, Kasarda (2013) suggests three other possibilities, including 1) increased wheat consumption; 2) use of gluten as a food additive; 3) improved CD diagnosis.
Therefore, it is useful to confirm or refute whether gluten has fundamentally changed in the last 100 years or so (the “modern era”).
In this historical study, we applied an alternative approach to investigate if there were any potential changes across time in the composition of gluten. Our approach, via RP-HPLC, constituted a coarse-grained way to investigate potential changes in gliadins that may lead to improved targeting of fine-grained analyses of potential allele-level changes (e.g. similar to the antibody-based work of Van den Broeck et al. (2010), or the LC-MALDI-
TOF-MS work of Mejias et al. (2014)). We wanted to begin to address whether breeding for higher dough strength or for reduced plant height may have been associated with increased CD prevalence. Our hypothesis is that if gluten has changed systematically over time, or with the introduction of the dwarfing alleles, we should be able to observe 84 systematic or stepwise changes in gliadins in high production wheat varieties grown over the last 110 years. Some strategies have already been applied, e.g. an attempt at modification or deletion of toxic gluten genes, so as to generate the celiac-safe wheat
(van den Broeck et al., 2009; Mitea et al., 2010; van den Broeck et al., 2011).
4.2 Materials and methods
4.2.1 Materials
Sixty-two soft and 61 hard wheat varieties were collected. The varieties were top producers in commercial production in the Pacific Northwest (PNW) and elsewhere across the United States from 1900 to the present (with one variety from 1800)
(Appendix 2). “Top production” varieties were considered to be those grown over the most acreage in the U.S. over the last century based on information in from the World
Wheat Book (Bonjean and Angus, 2001), the National Agricultural Statistics Services
(NASS, 2012), and a draft copy of Commercial Wheat Varieties of the United States
(Kephart, 1991; NASS, 2012). If a variety was a top producer for many years, the second top production variety would be chosen to stand for that year. Three Australian spring wheat cultivars: “Baart”, “Federation” and “Hard Federation” were selected as they were abundantly planted in the 1930s. The variety “Sonora” dating back to 1800 was also chosen because it was tall and one of the oldest wheat varieties grown in the PNW. It was suggested by Davis (2011) that Sonora had a genotype that could not be recreated in modern wheats. Initially, public varieties were selected for easy access due to difficulties in obtaining germplasm under the Intellectual Property Rights restrictions. However, 19 85 cultivars (2000-2011) protected by the Plant Variety Protection Act (PVPA) were included in this experiment after receiving permission from the PVPA holders. In the end,
60 soft winter wheat cultivars including 8 club wheats and Sonora, 2 soft spring wheat cultivars (Baart and Federation), 60 hard winter wheat cultivars and 1 hard spring wheat cultivar (Hard Federation) were obtained through the USDA Small Grains Repository,
Aberdeen ID (USDA National Plant Germplasm System).
Seeds were planted in a greenhouse in 8x16 plug flats using the Sunshine Professional
Growing Mix #LA4P (Sun Gro Horticulture Canada Ltd., Canada), with 45 grams of
Osmocote Smart-Release® 14-14-14 NPK fertilizer (Scotts Miracle-Gro Company;
Marysville, OH) per bag. Until they reached the two-leaf stage, all plants were moved to a vernalization chamber (Hoffman Manufacturing Inc., Albany OR) and kept at a constant 4°C with 10 hours light per day for 10 weeks. After 85 days, all plants were transplanted into 1 gallon pots (1 plant per pot) containing the same soil mixture as the seed flats with four replicates. Pots were randomly placed under greenhouse conditions
(24°C for 10 hours at night and 29°C for 14 hours during the day) with each replication on a separated bench. Natural light was supplemented with sodium halide lights controlled by EnviroSTEP lighting system (Model M-4840; Wadsworth Control Systems,
Arvada CO), so as to provide artificial illumination during the 14 hours day period.
Plants were chemically treated for powdery mildew (Blumeria graminis f. sp. Tritici), aphid, and thrips (Frankliniella occidentalis) control approximately every two weeks. All heads were harvested and threshed at maturity. Whole-wheat flour samples were obtained by milling the mature grains through the Tecator Cemotec 1090 Sample Mill (FOSS, 86
Höganäs, Sweden) at grind level three. Between each sample, the mill was cleaned using a brush and canned compressed air. In this study one field replicate was selected for reversed-phased high-performance liquid chromatography (RP-HPLC) analysis as it was most complete with respect to the 123 varieties. All chemicals were analytical grade or better.
4.2.2 Gliadin analysis
Gliadins were separated from albumins, globulins, and glutenins by a sequential extraction procedure. RP-HPLC was used to separate different types of gliadins (α-/β-, γ- and ω) based on hydrophobicity (Wieser, 1998; Wieser et al., 1998; Mejias et al., 2014).
To extract and remove albumins and globulins, 0.2 g flour was suspended twice in 1mL of 0.4 M NaCl, shaken by vortexing (speed 1.5, about 200 rpm) (Vortex, VWR
International, Radnor, PA) for 20 min at ambient temperature, and centrifuged after each extraction at 13,000 g for 20 min (VWR International, Radnor, PA). The resulting supernatants were discarded. To extract the prolamins, the remaining pellet was then resuspended three times in 0.5 mL of 60% (v/v) ethanol and shaken by vortexing (speed
1.5, about 200 rpm) for 20 min at room temperature, followed by centrifugation at 13,000 g for 20 min. The supernatants were combined and filtered through a 0.45µm filter paper
(Pall Corporation, Ann Arbor, MI) prior to analysis.
Gliadin extracts were fractionated by RP-HPLC (Water 2695, Waters Corporation,
Milford, MA) using a C8 reverse-phase analytical column (5 µm particle size, 300 Å pore size, 4.6 mm inner diameter and 250 mm length: Zorbax 300SB-C8, Agilent 87
Technologies, Palo Alto, CA) and a guard column (KJ0-4280, Phenomenex, Torrance,
CA). Column temperature was maintained at 60°C. A linear elution gradient was created using two different mobile phases: deionized water containing 0.1% (v/v) trifluoroacetic acid (TFA) (solvent A, polar) and acetonitrile also with 0.1% (v/v) TFA (solvent B, non- polar). Filtered supernatants (90 μL) were injected and eluted with a linear gradient ranging from 20% to 60% of solvent B at a flow rate of 1.0 mL/min for 60 min. After each run, the column was washed with a solution of 90% solvent B for 10 min and equilibrated with a solution of 20% solvent B for another 10 min. Samples were detected spectroscopically at 214 nm using a Waters 2996 photodiode array detector (Waters
Corporation, Milford, MA). The chromatograms were separated into three parts at retention times 10-25 min (ω-gliadins), 25-33 min (α/β-gliadins) and 33-45 min (γ- gliadins) (Figure 4.1: Mejias et al. 2014).
Figure 4.1: Representative RP-HPLC chromatogram of gliadins. Three HPLC fractions collected at 10-25, 25-33 and 33-45 min were α/β-, ω- and γ-gliadins respectively.
4.2.3 RP-HPLC chromatogram data analyses
Absorbance data from RP-HPLC chromatograms were transformed and interpolated to
0.01-min intervals by cubic spline interpolation methods constructed from piecewise 88 third-order polynomials (Weisstein 2015) using an in-house program run in MATLAB
(v.6, The MathWorks, Natick, MA). The in-house MATLAB program was developed and run by co-author Ohm (e.g. Ohm et al., 2008). Each absorbance area (AA) was calculated by absorbance × time interval (0.01 min). The AA values for each retention time interval of 0.05 min were combined (700 values for 10-45 min run time). Since different varieties has different amount of proteins in total. To make results comparable, percentage AA
(%AA) values were used for data analysis and obtained from:
700 %퐴퐴푖 = 퐴퐴푖 / ∑ 퐴퐴푖 × 100 푖=1 where AAi is the area of ith 0.05 min time interval in the chromatogram (Ohm et al.,
2008). Mean values were taken from the duplicates of each variety. In addition, %AA mean values were combined based on three fractions at retention times of 10-25 min, 25-
33 min and 33-45 min thereby generating the relative amount of three gliadin types (ω-,
α/β- and γ-gliadins) for each variety.
4.2.4 Statistical analyses
Statistical analyses were performed using JMP 11 (SAS Institute Inc., Cary, NC) and
Microsoft Excel. Linear and polynomial regressions were applied to evaluate the relationship between decade of peak production and selected regions of the RP-HPLC chromatographs. Multiple comparisons of plant height between decades were calculated using Tukey’s HSD. One-way and multifactor analyses of variance (ANOVA) were performed to determine the significance of each main effect. For the RP-HPLC raw data from 4.2.3, further correlation analyses were performed between every one of the 700 89 individual %AA mean values (each ith 0.05 min time interval) and decade of peak production and average plant height. Correlation coefficients were shown as continuous spectra over RP-HPLC retention times. The significant differences for r and for f values were set at P ≤ 0.01 for all analyses.
4.3 Results and Discussion
4.3.1 Plant height change in decades
Table 4.1 (data obtained from Roseborough, 2014) indicates that there was a significant reduction of plant height from 1960 since the recent four decades grouped together with lower mean values. Therefore, incorporating dwarfing alleles into U.S. wheats reduced average plant height. One sample released in 1800 and four samples released after 2010 were included in 1910 and 2000 respectively due to the small sample sizes within these decades.
Table 4.1: Average plant height (cm) by decade.
Level Mean 1910 117.9A* 1920 129.5A 1930 127.3A 1940 111.6A 1950 109.2A 1960 112.9A 1970 101.1B 1980 93.7B 1990 102.5B 2000 81.1B * Levels not connected by same letter are significantly different.
90
Table 4.2: Analyses of Variance for the effects of soft versus hard and tall versus dwarf wheats on proportional abundance of different gliadin fractions. ω - gliadin α/β - gliadin γ - gliadin Factor F ratio P > F F ratio P > F F ratio P > F Hard / Soft 55.4 < 0.0001 1.4 0.236 17.7 < 0.0001 Tall / Dwarf 1.8 0.178 2.2 0.144 3.7 0.058
Table 4.3: Mean and Standard Error for proportional abundance of different gliadin fractions for soft versus hard and tall versus dwarf wheats.
ω - gliadin α/β - gliadin γ - gliadin Factor Mean SE** Mean SE Mean SE Hard 8.4A* 0.23 48.5A 0.51 43.1A 0.55 Soft 6.0B 0.23 47.7A 0.50 46.4B 0.55 Tall 7.5A 0.34 48.8A 0.61 43.7A 0.71 Dwarf 7.0A 0.24 47.7A 0.43 45.3A 0.50 * Levels not connected by same letter within the hard vs. soft and tall vs. dwarf categories are significantly different. ** SEs and means comparisons for the effects of kernel hardness and dwarfing alleles on gliadins were obtained from 1-WAY ANOVA.
91
4.3.2 Effects of kernel hardness and dwarfing alleles on gliadin fractions
Factor: hard versus soft
ANOVA (Table 4.2) indicated that kernel harness had a strong effect on ω- and γ- gliadins, but not α/β-gliadins. Table 4.3 shows that soft wheats had lower relative abundance of ω-gliadins and higher relative abundance of γ-gliadins compared to hard wheats. No association was found between kernel hardness and α/β-gliadins as similar amounts of α/β-gliadins were found in both hard and soft wheats. This suggests that breeding selectively for hard or soft kernel texture did not differentially increase the relative amounts of α/β-gliadins across the last 110 years.
Factor: tall versus dwarf
ANOVA (Tables 4.2, 4.3) also demonstrated that dwarfing alleles did not influence the relative abundance of the different types of gliadins as there were no significant differences between tall and dwarf types. This indicates that there was no stepwise change in gliadin proteins after deployment of the dwarfing alleles. This is opposite to
Willam Davis’s (2011) speculation that “increased gluten exposure from modern dwarf wheat has contributed to the increase in celiac disease”.
4.3.3 Changes in gliadin fractions over time
To do this analysis gliadins were integrated to find the relative abundance of each gliadin type (10-25 min (ω-gliadins), 25-33 min (α/β-gliadins) and 33-45 min (γ-gliadins): Figure
4.1) for each variety. For each decade the average relative abundance of each gliadin fraction across all varieties grown in that decade was determined to used as the response 92 variable (Figure 4.2). The soft variety “Sonora” released in 1800 was included in the decade 1900 to facilitate in statistical analysis. Changes in ω-gliadins were best modeled using linear regression, while α/β- and γ-gliadins were best modeled using a 2nd order polynomial regression.
Across all samples ω-gliadins had a slight but not significant at P < 0.01 increase over time based on the entire dataset (r = 0.64, P = 0.024). When data was split into soft and hard categories, there was no significant change in relative amounts of ω-gliadins over time for soft wheats (r = 0.40, P ≤ 0.01) or hard wheats (r = 0.15, P ≤ 0.01).
Across all data α/β-gliadins increased until 1960, followed by a decrease (2nd order polynomial fit r = 0.85, P ≤ 0.01). When looking at soft and hard data individually,
Similar trends were observed in soft wheats (r = 0.73, P ≤ 0.01) and hard wheats (r =
0.74, P ≤ 0.01), but a greater decline after 1960 was shown in hard wheats.
In contrast to α/β-gliadins, γ-gliadins showed the opposite trend, with a reduction in relative abundance until 1960 and a subsequent increase (2nd order polynomial fit r =
0.85, P ≤ 0.01). Similar trends were also observed in soft wheats (r = 0.66, P ≤ 0.01) and hard wheats (r = 0.61, P ≤ 0.01), but hard wheats showed a more obvious increase from
1960.
Several things can be summarized in terms of the above results: 1) For all wheat samples, relative abundance ω-gliadins did not change over time, α/β-gliadins first increase in 93 relative abundance then decrease, γ-gliadins first decrease in relative abundance then increase. 2) Soft and hard wheats had similarly varying patterns in all types of gliadins. 3)
Hard wheats had a more noticeable decrease in α/β-gliadins and increase in γ-gliadins after 1960. The stepwise change in 1960 questioned the association between CD increase and breeding for increased dough strength in hard wheats, since the relative abundance of
α/β-gliadins did not keep going up, and α-gliadin is considered the major trigger force for
CD initiation.
Figure 4.2: Linear and polynomial regressions of proportional abundance different gliadin fractions across decades for the entire dataset, and for soft wheats and hard wheats viewed separately.
94
4.3.4 Linear correlations between decade of production, and plant height versus % absorbance area of gliadins separated by RP-HPLC
Currently, gliadin studies by RP-HPLC often focus on large fractions (Mejias et al.,
2014), specifically, on integrations of the three main fractions, ω-, α/β- and γ-gliadins
(Wieser, 1998). In this correlation study, instead of integrating e.g. between 10 and 25 minutes for the ω-gliadins, we integrated the gliadin chromatogram in 0.05 minute slices.
The %AA of each slice was calculated for all varieties and each slice was correlated with the response variable, decade of production, or plant height. In this was we were able to let the chromatogram indicate which fractions or regions of the chromatogram appeared to change systematically over time. The benefit of these analyses is to allow fine-grained analyses (e.g. immunological, marker, or LC-MS) of specific gliadin fractions that were seen to change across time, rather than to arbitrarily select specific gliadins to track. Thus, our method is more targeted and valuable. Figures 4.3 and 4.4 show that both decade and plant height had significant linear correlations with specific regions of the gliadin chromatograms. The limitation here is that as a result of the very large data set we were limited to simple linear correlations. Figure 4.2 shows however, that there may be some curvilinear relations that are not well modeled.
Decades of peak production
For all data (Figure 4.3A), decades of peak production had significant positive correlations with %AA values of protein fractions eluted at approximately 10, 12, 15, 17,
20-24, 28 and 43-44 min, and had negative correlations at around 10, 23, 32-34, 39 and 95
43 min (Table 4.4), suggesting that all three types of gliadins changed over time. A/β- and γ-gliadins were observed to have more changes than ω-gliadins.
For soft wheats (Figure 4.3B), %AA values of protein fractions eluted at around 11-13,
15, 21, 28 and 43 min showed positive correlations with decades of peak production, and fractions eluted at about 23, 35, 42-43 min showed negative correlations (Table 4.4). Ω- gliadins changed more overtime than the other two types, α/β- and γ-gliadins had similar degrees of change over time in soft wheats.
For hard wheats (Figure 4.3C) there were many fewer significant positive or negative correlations compared to the soft wheats (Figure 4.3B). There was a positive relationship between decades of peak production and %AA values of protein fractions eluted at about
29 min, and negative relationship was shown at approximately 32, 36 and 43 min (Table
4.4). This indicates that there is no change in ω-gliadins over time and very tiny changes were observed in α/β- and γ-gliadins.
Plant height
For all data (Figure 4.4A), significant positive correlations were observed between plant height and %AA values of protein fractions eluted at around 23, 33-37 and 43 min.
Significant negative correlations were observed at about 14, 21, 25 and 27-29 min (Table
4.4). This indicates that all three types of gliadins are related to plant height and more interactions were shown in γ-gliadins compared with α/β- and ω-gliadins.
96
For soft wheats (Figure 4.4B), similar correlations were obtained as shown in all data
(Figure 4.4A). %AA values of protein fractions eluted at approximately 23, 33-38, 40 and
42-43 min showed positive correlations with plant height, and those eluted at about 12-13,
25, 27-28, 30-31 and 39-40 min had negative correlations (Table 4.4). All three types of gliadins associated with plant height and γ-gliadins showed more interactions compared with α/β- and ω-gliadins.
For hard wheats (Figure 4.4C), there were many fewer significant positive or negative correlations compared to the soft wheats (Figure 4.4B). There was a positive relationship between plant height and %AA values of protein fractions eluted at about 36 and 43 min, and negative relationship was observed at around 14 and 24 min (Table 4.4). This suggests that barely interactions between plant height and gliadins occur in hard wheats.
Although ANOVA shows dwarfing alleles had no interaction with the three major gliadin fractions, some specific regions in soft data related to plant height were observed in these correlograms. The significant correlations seen in the overall data (Figures 4.3 and 4.4) were driven mostly by changes in the soft wheat population. In this analysis deliberate breeding for high dough strength and incorporating dwarfing alleles appears not to have systematically changed the relative proportions of the three gliadin types in hard wheats.
This is in contrast to the suggestions of van den Broeck et al. (2010) and to the sensationalist speculations of Davis (2011).
97
The different analyses done to generate Figures 4.2 and 4.3/4.4 lead to different conclusions. More changes in hard wheats across time in Figure 4.2, and more changes across time and plant height for soft wheats in Figure 4.3/4.4. The discrepancy may be related to the use of linear correlations only to create correlograms when some relationships are apparently non-linear.
98
P = ± 0.01 Retention time axis
Chromatogram integrations at 25 min and 33 min
Chromatogram Correlation coefficients (r)
A
Absorbance
Value
(AU)
r r
B
Absorbance
Value
r r
(AU)
C
Absorbance
Value
(AU)
r r
Retention Time (min)
Figure 4.3: Plots (correlograms) of linear correlation coefficients (r) for decades of peak production versus %AA of protein extracts for each 0.05 min time interval as separated by RP-HPLC for the entire dataset (A), soft wheats (B) and hard wheats (C). A representative chromatogram is shown as for reference purposes.
99
P = ± 0.01 Retention time axis
Chromatogram integrations at 25 min and 33 min
Chromatogram Correlation coefficients (r)
A
Absorbance
Value
(AU)
r r
B
Absorbance
Value
r r
(AU)
C
Absorbance
Value
r r
(AU)
Retention Time (min)
Figure 4.4: Plots (correlograms) of linear correlation coefficients (r) for plant height versus %AA of protein extracts for each 0.05 min time interval as separated by RP- HPLC for the entire dataset (A), soft wheats (B) and hard wheats (C). A representative chromatogram is shown as for reference purposes.
100
Table 4.4: Fractions of significant correlation (report as retention time fractions in minute) for decade of peak production, and plant height versus %AA of protein extracts for each 0.05 min time interval as separated by RP-HPLC in the entire dataset, soft wheats and hard wheats correlograms. Decade Plant Height All Soft Hard All Soft Hard 10.00 – 10.05 11.65 – 11.70 29.15 – 29.20 23.20 – 23.25 23.15 – 23.25 36.80 – 36.95 12.20 – 12.70 11.85 – 11.90 23.30 – 23.35 23.70 – 24.05 43.70 – 43.85 15.05 – 15.20 12.15 – 12.80 23.80 – 23.90 33.60 – 33.65 17.95 – 18.25 13.15 – 13.40 33.50 – 33.75 34.20 – 34.30 20.00 – 20.20 13.85 – 14.35 34.25 – 34.30 34.65 – 34.80 21.80 – 21.90 15.10 – 15.40 34.70 – 34.80 35.75 – 36.00 Positive 22.35 – 22.45 21.80 – 22.00 35.75 – 36.05 36.30 – 36.75 correlation 24.15 – 27.60 25.50 – 26.75 36.25 – 36.50 36.05 – 37.45 28.80 – 29.00 28.60 – 28.65 36.65 – 37.50 37.80 – 37.90 43.05 – 43.10 43.05 – 43.50 37.75 – 38.30 38.20 – 38.35 44.45 – 44.65 43.80 – 44.10 38.50 – 38.65 40.85 – 41.00 42.65 – 42.80 43.90 – 44.35 10.35 – 10.55 23.85 – 24.00 32.25 – 33.40 14.83 – 14.90 12.48 – 12.50 14.80 – 14.85 23.25 – 23.30 35.45 – 35.60 36.70 – 37.40 21.30 – 21.35 13.30 – 13.35 24.55 – 24.65 32.20 – 32.85 42.50 – 43.00 43.75 – 44.10 21.75 – 21.80 25.30 – 25.55 33.30 – 33.40 43.80 – 44.35 25.30 – 25.65 27.80 – 27.85 Negative 33.65 – 34.00 27.80 – 27.85 28.25 – 28.30 correlation 34.90 – 37.45 28.25 – 28.30 28.70 – 28.90 39.85 – 40.00 28.65 – 28.90 30.65 – 30.70 43.60 – 44.15 29.60 – 29.65 31.85 – 31.95 39.65 - 39.80 40.35 – 40.40 101
4.4 Conclusions
Incorporation of new dwarfing alleles into the U.S. wheat breeding after 1960 reduced plant height.
ANOVA suggested that wheat breeding for higher dough strength had not selectively increased the relative abundance of α/β-gliadins across the last 110 years, but had altered the relative abundance of the other two fractions. ANOVA results indicated no change in proportions of the three gliadin fractions after deployment of the dwarfing alleles suggesting the tall to dwarf change was independent of gluten composition. Second order polynomial regression analyses showed that the relative abundance of α/β-gliadins increased until around 1960 then decreased. The changes were more noticable in the hard wheats. The converse was observed for 훾-gliadins. This stepwise change questioned the association between CD increase and breeding for increased dough strength in hard wheats, since the relative abundance of α/β-gliadins did not keep going up, and α-gliadin is considered the major trigger force for CD initiation. In contrast, linear correlation analyses with each of 700, three second long fractions of the RP-HPLC chromatograms suggested that most changes were related to the soft wheat population. The discrepancy between the regression analyses of the three major fractions and the 700 small fractions may be related to the use of linear correlations in the latter when some relationships were clearly non-linear.
Overall, our results did not fully support speculations that there have been profound changes in gluten composition related to the dwarfing alleles or selection for increased 102 dough strength in hard wheats. Our approach, via RP-HPLC, constituted a coarse-grained way to investigate potential changes in gliadins that may lead to improved targeting of fine-grained analyses (e.g. immunological, marker, or LC-MS) of specific gliadin fractions (Figure 4.3, Table 4.4) that were seen to change across time, rather than to arbitrarily select specific gliadins to track. Due to the limited scope of this study, further analysis is needed to evaluate if there are any potential relationships between CD and evolution of gliadins, e.g. enlarging the sample population, testing spring wheat, and directly testing the CD pathogenic epitopes by sequencing.
103
References
Barrett, J. S., Gibson, P. R., 2012. Fermentable oligosaccharides, disaccharides, monosaccharides and polyols (fodmaps) and nonallergic food intolerance: fodmaps or food chemicals? Therap. Adv. Gastroenterol. 5:261-268.
Biesiekierski, J. R., Newnham, E. D., Irving, P. M., Barrett, J. S., Haines, M. H., Doecke, J. D., Shepherd, S. J., Muir, J. G., Gibson, P. R. 2011. Gluten causes gastrointestinal symptoms in subjects without celiac disease: a double-blind randomized placebo-controlled trial. Am. J. Gasteroenterol. 106:508-514.
Biesiekierski, J. R., Peters, S. L., Newnham, E. D., Rosella, O., Muir, J. G., Gibson, P. R. 2013. No effects of gluten in patients with self-reported non-celiac gluten sensitivity after dietary reduction of fermentable, poorly absorbed, short-chain carbohydrates. Gasteroenterol. 145:320-328.
Bonjean, A. P., Angus, W. J. 2001. "The world wheat book: a history of wheat breeding," Lavoisier Publishing, Paris.
Bonomi, F., Iametti, S, Mamone, G., Ferranti, P. 2013. The performing protein: beyond wheat proteomics? Cereal Chem. 90:358-366.
Brouns, F. J. P. H., van Buul, V. J., Shewry, P. R. 2013. Does wheat make us fat and sick? J. Cereal Sci. 58:209-215.
Carroccio, A., Mansueto, P., Iacono, G., Soresi, M., D’Alcamo, A., Cavataio, F., Brusca, I., Florena, A.M., Ambrosiano, G., Seidita, A., Pirrone, G., Rini, G. B. 2012. Non- celiac wheat sensitivity diagnosed by double-blind placebo-controlled challenge: exploring a new clinical entity. Am. J. Gasteroenterol. 107:1898-1906.
Davis, W. R. 2011. Wheat Belly: lose the wheat, lose the weight, and find your path back to health. Rodale Books. Emmaus, PA., pp. 79-82.
Delcour, J. A., Hoseney, R. C., 2010. Proteins of cereals. In: Principles of cereal science and technology. AACC International, Inc., St. Paul, MN. pp. 53-70.
Delcour, J. A., Joye, I. J., Pareyt, B., Wilderjans, E., Brijs, K., Lagrain, B. 2012. Wheat gluten functionality as a quality determinant in cereal-based food products. Annu. Rev. Food Sci. Technol. 3:469-492.
Dewar, D., Pereira, S. P., Ciclitira, P. J. 2004. The pathogenesis of coeliac disease. Intern. J. Biochem. Cell Biol. 36:17-24.
104
Dieterich, W., Ehnis, T., Bauer, M., Donner, P., Volta, U., Riecken, E. O., Schuppan, D. 1997. Identification of tissue transglutaminase as the autoantigen of celiac disease. Nat. Med. 3:797-801.
El-Salhy, M., Gilja, O. H., Gundersen, D., Hatlebakk, J. G., Hausken, T. 2014. Interaction between ingested nutrients and gut endocrine cells in patients with irritable bowel syndrome. Int. J. Mol. Med. 34:363-371.
Fasano, A., Berti, I., Gerarduzzi, T., Not, T., Colletti, R. B., Drago, S., Elitsur, Y., Green, P. H. R., Guandalini, S., Hill, I. D., Pietzak, M., Ventura, A., Thorpe, M., Kryszak, D., Fornaroli, F., Wasserman, S. S., Murray, J. A., Horvath, K. 2003. Prevalence of celiac disease in at-risk and not-at-risk groups in the United States: a large multicenter study. Arch. Intern. Med.163:286-292.
Fleckenstein, B., Qiao, S. W., Larsen, M. R., Jung, G., Roepstorff, P., Solid, L. M. 2004. Molecular characterization of covalent complexes between tissue transglutaminase and gliadin peptides. J. Biol. Chem. 279:17607-17616.
Karell, K., Louka, A. S., Moodie, S. J., Ascher, H., Clot, F., Greco, L., Ciclitira, P. J., Sollid, L. M., Partanen, J. 2003. HLA types in celiac disease patients not carrying the DQA1*05-DQB1*02 (DQ2) heterodimer: results from the European Genetics Cluster on Celiac Disease. Hum. Immunol. 64:469-477.
Kasarda, D. D. 2013. Can an increase in celiac disease be attributed to an increase in the gluten content of wheat as a consequence of wheat breeding? J. Agric. Food Chem. 61:1155-1159.
Kephart, K. D. 1991. Commercial Wheat Varieties of the United States. Wheat Industry Resource Committee.
Khamsi, R. 2014. “ Gluten sensitivity” may be a misnomer for distinct illness to various wheat proteins. Sci. Am. 310:2.
Mejias, J. H., Lu, X., Osorio, C., Ullman, J. L., von Wettstein, D., Rustgi, S. 2014. Analysis of wheat prolamins, the causative agents of celiac sprue, using reversed phase high performance liquid chromatography (RP-HPLC) and matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). Nutrients. 6:1578-1597.
Meresse, B., Ripoche, J., Heyman, M., Cerf-Bensussan, N. 2009. Celiac disease: from oral tolerance to intestinal inflammation, autoimmunity and lymphomagenesis. Mucosal Immuol. 2:8-23.
Mitea, C., Salentijn, E. M. J., van Veelen, P., Goryunova, S. V., van der Meer, I. M., van den Broeck, H. C., Mujico, J. R., Monserrat, V., Gilissen, L. J. W. J., Drijfhout, J. 105
W., Dekking, L., Koning, F., Smulders, M. J. M. 2010. A universal approach to eliminate antigenic properties of alpha-gliadin peptides in celiac disease. PLoS One. 5.
Mowat, A. M. 2003. Coeliac disease – a meeting point for genetics, immunology, and protein chemistry. Rapid Rev. 361:1290-1292.
Mustalahti, K., Catassi, C., Reunanen, A., Fabiani, E., Heier, M., McMillan, S., Murray, L., Metzger, M.H., Gasparin, M., Bravi, E., 2010. The prevalence of celiac disease in europe: results of a centralized, international mass screening project. Ann. Med. 42:587-595.
NASS. 2012. "National Agricultural Statistics Service." United States Department of Agriculture.
Ohm, J. B., Ross, A. S., Peterson, C. J., Ong, Y.-L. 2008. Relationships of high molecular weight glutenin subunit composition and molecular weight distribution of wheat flour protein with water absorption and color characteristics of noodle dough. Cereal Chem. 85:123-131.
Ram, M. 2014. "Breeding Methods," Asoke K. Ghosh, PHI Learning Private Limited, Delhi.
Roseborough, C. 2014. A historical evaluation of high molecular weight glutenins and the dwarfing (Rht) gene in Triticum aestivum in selected genotypes grown in the United States. [MSc dissertation]. OR, USA: Oregon State Univ. 111 p. Available from: ScholarsArchive online.
Rubio-Tapia, A., Kyle, R. A., Kaplan, E. L., Johnson, D. R., Page, W., Erdtmann, F., Brantner, T. L., Kim, W., Phelps, T. K., Lahr, B. D. 2009. Increased prevalence and mortality in undiagnosed celiac disease. Gastroenterol. 137:88-93.
Sapone, A., Bai, J. C., Ciacci, C., Dolinsek, J., Green, P. H. R., Hadjivassiliou, M., Kaukinen, K., Rostami, K., Sanders, D. S., Schumann, M., Ullrich, R., Villalta, D., Volta, U., Catassi, C., Fasano, A. 2012. Specturm of gluten-related disorders: consensus on new nomenclature and classification. BMC Medicine. 10:13-25.
Shan, L., Molberg, O., Parrot, I., Hausch, F., Filiz, F., Gray, G. M., Sollid, L. M., Khosla, C. 2002. Structural basis for gluten intolerance in celiac sprue. Science. 297:2275- 2279.
Shewry, P. R. 2009. Wheat. J. Experimental. Botany. 60:1537-1553.
Tonutti, E., Bizzaro, N. 2014. Diagnosis and classification of celiac disease and gluten sensitivity. Autoimmun. Rev. 13:472-476. 106 van den Broeck, H. C., van Herpen, T. W. J. M., Schuit, C., Salentijn, E. M. J., Dekking, L., Bosch, D., Hamer, R. J., Smulders, M. J. M., Gilissen, L. J. W. J., van der Meer, I. M. 2009. Removing celiac disease-related gluten proteins from bread wheat while retaining technological properties: a study with Chinese Spring deletion lines. BMC Plant Biol. 9:41. van den Broeck, H. C., de Jong, H. C., Salentijn, E. M. J., Dekking, L., Bosch, D., Hamer, R. J., Gilissen, L. J. W. J., van der Meer, I. M., Smulders, M. J. M. 2010. Presence of celiac disease epitopes in modern and old hexaploid wheat varieties: wheat breeding may have contributed to increased prevalence of celiac disease. Theor. Appl. Genet. 121:1527-1539. van den Broeck, H. C., Smulders, M. J. M., Hamer, R. J., Gilissen, L. J. W. J., van der Meer, I. M. 2011. Coeliac-safe wheat. A novel wheat to decrease the prevalence and symptoms of coeliac disease. Food Safety. 22:18-21.
Verdu, E. F., Armstrong, D., Murray, J. A. 2009. Between celiac disease and irritable bowel syndrome: the “no man’s land” of gluten sensitivity. Am. J. Gasteroenterol. 104:1587-1594.
Vogel, O. A., R. E. Allan, and C. J. Peterson. 1963. Plant and performance characteristics of semidwarf winter wheats producing most efficiently in eastern Washington. Agron. J. 55:397-398.
Weisstein, E. W. 2015. "Cubic Spline." From MathWorld--A Wolfram Web Resource.
Wieser, H. 1998. Investigations on the extractability of gluten proteins from wheat bread in comparison with flour. Z Lebensm Unters Forsch A. 207:128-132.
Wieser, H., Antes, S., Seilmeier, W. 1998. Quantitative determination of gluten protein types in wheat flour by reversed-phase high-performance liquid chromatography. Cereal Chem. 75:644-650.
Wieser, H., Koehler, P. 2008. The biochemical basis of celiac disease. Cereal Chem. 85:1-13.
Wieser, H. Konitzer, K., Koehler, P. 2012. Celiac disease – multidisciplinary approaches. Cereal Food. World. 57:215-224.
107
Chapter 5: General Conclusion
Gluten functionality study
Gluten protein composition and GPC both play an important role in protein heatset behaviors, and served as non-amylase modulators of FN. For the group with constant
GPC, %LUPP is positively correlated to FN. For the group where GPC varied widely, both GPC and %LUPP play an important role in FN, with positive associations between
FN versus GPC and %LUPP.
However, our hypothesis “longer gluten polypeptides are more flexible and would therefore heatset faster than shorter ones” cannot be demonstrated. In contrast, our results showed that samples with lower MWD had faster heatset times than samples with higher
MWD according to a controlled heating test. We also demonstrated that kernel hardness did not affect FN as differentiated by initial flour particle size. The significant correlation between kernel hardness and FN is probably due to the commercial selection for strong dough strength with high protein content in hard wheat. The relationship between wheat protein and FN remains unclear at this point and requires further investigation. Our current technique by obtaining relative amount of gluten proteins (%LUPP) is not sufficient to evaluate underlying mechanisms of biopolymer behavior in a flour-water system at a molecular level. This work will be followed by: measuring the FN in reducing agent that stops all disulfide bonding. We can also use other techniques (e.g., FTIR) to observe protein aggregation behavior by detecting the formation/reduction of disulfide linkage. On the other hand, to reduce the complexity of the flour-water system and 108 eliminate the potential interference, it is necessary to extract starch and observe starch and protein behaviors separately in model systems.
Gluten historical study
We chose an alternative, coarse-grained, way to investigate potential changes in gliadins that may lead to improved targeting of fine-grained analyses of potential allele-level changes. ANOVA suggested that wheat breeding for higher dough strength had not selectively increased the relative abundance of α/β-gliadins across the last 110 years, but had altered the relative abundance of the other two fractions. ANOVA results indicated no change in proportions of the three gliadin fractions after deployment of the dwarfing alleles suggesting the tall to dwarf change was independent of gluten composition.
Second order polynomial regression analyses showed that the relative abundance of α/β- gliadins increased until around 1960 then decreased. The changes were more noticable in the hard wheats. The converse was observed for 훾-gliadins. This stepwise change questioned the association between CD increase and breeding for increased dough strength in hard wheats, since the relative abundance of α/β-gliadins did not keep going up, and α-gliadin is considered the major trigger force for CD initiation. In contrast, linear correlation analyses with each of 700, three second long fractions of the RP-HPLC chromatograms suggested that most changes were related to the soft wheat population.
The discrepancy between the regression analyses of the three major fractions and the 700 small fractions may be related to the use of linear correlations in the latter when some relationships were clearly non-linear.
109
Overall, our results did not fully support speculations that there have been profound changes in gluten composition related to the dwarfing alleles or selection for increased dough strength in hard wheats. Due to the limited scope of this study, further analysis is needed to evaluate if there are any potential relationships between CD and evolution of gliadins, e.g. enlarging the sample population, applying spring wheat, and directly testing the CD pathogenic epitopes by sequencing.
110
Bibliography
AACC International. 2010. Approved Methods of Analysis, 11th Ed. Methods 22-08.01, 39-11.01, 54-40.02, 55-31.01 and 56-81.03. Available online only. AACC International, Inc., St. Paul, MN.
Aalami, M., Leelavathi, K., Prasada Rao, U. J. S. 2007. Spagheii making potential of Indian durum wheat species in relation to their protein, yellow pigment and enzyme contents. Food Chem. 100: 1243-1248.
Anderson, O. D, Green, F. C., Yip, R. E., Halford, N. G., Shewry, P. R., Malpica-Romero, J. M. 1989. Nucleotide sequences of the two high-molecular-weight glutenin genes from the D-genome of a hexaploid bread wheat, Triticum aestivum L. cv Cheyenne. Nucleic Acids Res. 17:461-462.
Anjum, F. M., Walker, C. E. 1991. Review on the significance of starch and protein to wheat kernel hardness. J. Sci. Food Agr. 56:1-13.
Atwell, W. A., Hood, L. F., Lineback, D. R., Varriano-Marston, E., Zobel, H. F. 1988. The terminology and methodology associated with basic starch phenomena. Cereal Food. World. 33:306-311.
Atwell, W. A. 2001a. An overview of wheat development, cultivation and production. Cereal Food. World. 46:59-62.
Atwell, W. A. 2001b. Wheat Flour Handbook (Eagan Press). AACC International, Inc., St. Paul, MN.
Bacic, A., Stone, B. A. 1981a. Chemistry and organization of aleurone cell-wall components from wheat and barley. Aust. J. Plant Physiol. 8:475-495.
Bacic, A., Stone, B. A. 1981b. Isolation and ultrastructure of aleurone cell-walls from wheat and barley. Aust. J. Plant Physiol. 8:453-474.
Barrett, J. S., Gibson, P. R. 2012. Fermentable oligosaccharides, disaccharides, monosaccharides and polyols (fodmaps) and nonallergic food intolerance: fodmaps or food chemicals? Therap. Adv. Gastroenterol. 5:261-268.
Bason, M. L., Blakeney, A. B. 2009. Grain and grain products. In: Crosbie, G. B., Ross, A. S. (Eds.), The RVA handbook. AACC International, Inc., St. Paul, MN. pp. 119-130.
Bass, E. J. 1988. Wheat: chemistry and technology. In: Pomeranz, Y., Hlynka, I. (Eds.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 1- 68. 111
Bartels, D., Altosaar, I., Harberd, N. P., Barker, R. F., Thompson, R. D. 1986. Molecular analysis of γ-gliadin gene families at the com- plex Gli-1 locus of bread wheat (T. aestivum L.). Theor. Appl. Genet. 72:845-853.
Bechtel, D. B., Shewry, P. R., Abecassis, J., Evers, A. D. 2009. Development, structure, and mechanical properties of the wheat grain. In: Khan, K., Shewry, P. R. (Eds.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 51-95.
Beresford, G., Stone, B. A. 1983. (1→3), (1→4)-Beta-D-Glucan Content of Triticum Grains. J. Cereal Sci. 1:111-114.
Batey, I. L. 2007. Interpretation of RVA curves. In: Crosbie, G. B., Ross, A. S. (Eds.), The RVA handbook. AACC International, Inc., St. Paul, MN. pp. 19-30.
Bean, S. R., Bietz, J. A., Lookhart, G. L. 1998. High-performance capillary electrophoresis of cereal proteins. J. Chromatogr. A. 814:25- 41.
Belton, P. S. 1999. On the elasticity of wheat gluten. J. Cereal Sci. 29:103–107.
BeMiller, J. N., Huber, K. C. 2007. Carbohydrates. In: Damodaran S., Parkin K. L., Fennema O. R. (Eds.), Fennema's Food Chemistry. CRC press/Taylor & Francis Group. Boca Raton, FL, pp. 83-154.
Bettge, A. D., Morris, C. F. 2007. Oxidative gelation measurement and influence on soft wheat batter viscosity and end-use quality. Cereal Chem. 84:237-242.
Biesiekierski, J. R., Newnham, E. D., Irving, P. M., Barrett, J. S., Haines, M. H., Doecke, J. D., Shepherd, S. J., Muir, J. G., Gibson, P. R. 2011. Gluten causes gastrointestinal symptoms in subjects without celiac disease: a double-blind randomized placebo-controlled trial. Am. J. Gasteroenterol. 106:508-514.
Biesiekierski, J. R., Peters, S. L., Newnham, E. D., Rosella, O., Muir, J. G., Gibson, P. R. 2013. No effects of gluten in patients with self-reported non-celiac gluten sensitivity after dietary reduction of fermentable, poorly absorbed, short-chain carbohydrates. Gasteroenterol. 145:320-328.
Bietz, J. A. 1983. Separation of cereal proteins by reversed-phase high-performance liquid chromatography. J. Chromatogr. A. 255:219-238.
Blackwood, A. D., Salter, J., Dettmar, P. W., Chaplin, M. F. 2000. Dietary fibre, physicochemical properties and their relationship to health. J. R. Soc. Promot. Health. 120:242–247.
112
Bonjean, A. P., Angus, W. J. 2001. "The world wheat book: a history of wheat breeding," Lavoisier Publishing, Paris.
Bonomi, F., Iametti, S, Mamone, G., Ferranti, P. 2013. The performing protein: beyond wheat proteomics? Cereal Chem. 90:358-366.
Bowles, L. K. 1996. Amylolytic enzymes. In Hebeda, R. E., Zobel H. F. (Eds.), Baked goods freshness: technology, evaluation, and inhibition of staling. Marcel Dekker, Inc., New York. pp. 105–129.
Bradova, J., Matejova, E. 2008. Comparison of the results of SDS PAGE and chip electrophoresis of wheat storage proteins. Chromatographia Supplement. 67:S83- S88.
Brijs, K., Courtin, C. M., Goesaert, H., Gebruers, K., Delcour, J. A., Shewry, P. R., Hennry, R. J., Nicolas, J., Potus, J., Garcia, R., Davidou, S. 2009. Enzymes and enzyme inhibitors endogenous to wheat. In: Khan, K., Shewry, P. R. (Eds.), Wheat: Chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 401-435.
Brouns, F. J. P. H., van Buul, V. J., Shewry, P. R. 2013. Does wheat make us fat and sick? J. Cereal Sci. 58:209-215.
Buléon, A., Colonna, P., Planchot, V., Ball, S. 1998. Starch granules: Structure and biosynthesis. Int. J. Biol. Macromol. 23:85-112.
Buléon, A., Colonna, P. 2007. Physicochemical behaviour of starch in food applications. In: Belton, P. S. (Ed.), The chemical physics of food. Blackwell Pub.: Oxford; Ames, IA. pp. 20-67.
Buliga, G. S., Brant, D. A., Fincher, G. B. 1986. The sequence statistics and solution conformation of a barley (1→3), (1→4)-β-D-glucan. Carbohydr. Res. 157:139- 156.
Carroccio, A., Mansueto, P., Iacono, G., Soresi, M., D’Alcamo, A., Cavataio, F., Brusca, I., Florena, A. M., Ambrosiano, G., Seidita, A., Pirrone, G., Rini, G. B. 2012. Non-celiac wheat sensitivity diagnosed by double-blind placebo-controlled challenge: exploring a new clinical entity. Am. J. Gasteroenterol. 107:1898-1906.
Carson, G. R., Edwards, N. M. 2009. Criteria of wheat and flour quality. In: Khan, K., Shewry, P.R. (Eds.), Wheat: Chemistry and technology. AACC International, Inc., St. Paul, MN, pp. 97-118.
113
Causgrove, P., Causgrove, L., Peterson, B. 2004. Wheat and Flour Testing Methods – a guide to under standard wheat and flur quality. Wheat Marketing Center. Portland, OR.
Cauvain, S. P., Young, L.S., 2001. Flours baking problems solved. CRC Press, Boca Raton, FL, pp. 18-37.
Ciaffi, M., Tozzi, L., Lafiandra, D., 1996. Relationship between flour protein composition determined by size-exclusion high-performance liquid chromatography and dough rheological parameters. Cereal Chem. 73:346-351.
Ciccoritti, R. 2013. Content and relative composition of some phytochemicals in diploid, tetraploid and hexaploid Triticum species with potential nutraceutical properties. J. Cereal Sci. 57:200-206.
Colonna, P., Buléon, A. 1992. New insights on starch structure and properties. In: Cereal chemistry and technology: a long past and a bright future. In: Proceedings of the 9th international cereal and bread congress, Paris, France, Institut de Recherche Technologique Agroalimentaire des Céréales (IRTAC), pp. 25-42.
Courtin, C. M., Roelants, A., Delcour, J. A. 1999. Fractionation-reconstitution experiments provide insight into the role of endoxylanases in bread-making. J. Agric. Food Chem. 47:1870–1877.
Courtin, C. M., Delcour, J. A. 2002. Arabinoxylans and endoxylanases in wheat flour bread-making. J. Cereal Sci. 35:225-243.
Craven, M., Barnard, A., Otto, W., Labuschagne, M. T. 2007. Classification of South African bread wheat cultivars according to Hagberg falling number reaction to fertilizer treatment. Cereal Chem. 84:214- 219.
Curtis, B. C., 2002. Wheat in the world. In: Curtis, B. C., Rajaran, S., Macpherson, H. G. (Eds.), Bread wheat: improvement and production FAO, Rome, pp. 1-18.
Davis, W. R. 2011. Wheat Belly: lose the wheat, lose the weight, and find your path back to health. Rodale Books. Emmaus, PA. pp. 79-82.
Delcour, J. A., Hoseney, R. C., 2010a. Minor constituents. In: Principles of cereal science and technology. AACC International, Inc., St. Paul, MN. pp. 71-85.
Delcour, J. A., Hoseney, R. C., 2010b. Starch. In: Principles of cereal science and technology. AACC International, Inc., St. Paul, MN. pp. 23-51.
Delcour, J. A., Hoseney, R. C., 2010c. Proteins of cereals. In: Principles of cereal science and technology. AACC International, Inc., St. Paul, MN. pp. 53-70.
114
Delcour, J. A., Joye, I. J., Pareyt, B., Wilderjans, E., Brijs, K., Lagrain, B. 2012. Wheat gluten functionality as a quality determinant in cereal-based food products. Annu. Rev. Food Sci. Technol. 3:469-492.
Dervilly, G., Saulnier, L., Roger, P., Thibault, J. F. 2000. Isolation of homogeneous fractions from wheat water-soluble arabinoxylans. Influence of the structure on their macromolecular characteristics. J. Agric. Food Chem. 48:270–278.
Dewar, D., Pereira, S. P., Ciclitira, P. J. 2004. The pathogenesis of coeliac disease. Intern. J. Biochem. Cell Biol. 36:17-24.
Dexter, J. E., Sarkar, A. K., 2004. Dry milling. In: Wrigley, C. W., Corke, H., Walker, C. E. (Eds.), Encyclopedia of grain science. Elsevier Academic Press, Amsterdam; Boston, MA. pp. 363-375.
Dieterich, W., Ehnis, T., Bauer, M., Donner, P., Volta, U., Riecken, E. O., Schuppan, D. 1997. Identification of tissue transglutaminase as the autoantigen of celiac disease. Nat. Med. 3:797-801.
Dominguez, F., Cejudo, F. J. 1995. Patterns of endoproteolysis following wheat grain germination. Physiol. Plant. 95:253-259.
Donald, A. M., Waigh, T. A., Jenkins, P. J., Gidley, M. J., Debet, M., Smith, A. 1997. Internal structure of starch granules revealed by scattering studies. In: Frazier P. J., Donald A. M., Richmond P. (Eds.), Starch: structure and function. Cambridge: Royal Society of Chemistry. pp. 172-179.
Drapron, R., Godon, B. 1987. Role of enzymes in baking. In: Kruger, J. E., Lineback, D., Stauffer, C. E. (Eds.), Enzymes and their role in cereal technology. AACC International, Inc., St. Paul, MN. pp. 281-324.
Dunford, H. B., Stillman, J. S. 1976. On the function and mechanism of action of peoxidases. Coord. Chem. Rev. 19:187-251.
Dunnewind, B., van Vliet, T., Orsel, R. 2002. Effect of oxidative enzymes on bulk rheological properties of wheat flour doughs. J. Cereal Sci. 36:357-366.
Eliasson A. C., Larsson K. A. 1993. Physicochemical behavior of the components of wheat flour. In: Cereals in breadmaking: a molecular colloidal approach. Marcel Dekker, Inc., New York. pp. 31-160.
Eliasson, A. C., Gudmundsson, M. 1996. Starch: Physicochemical and functional aspects. In: Eliasson, A. C. (Ed.), Carbohydrates in food. Marcel Dekker, Inc., New York. pp. 431-503. 115
El-Salhy, M., Gilja, O. H., Gundersen, D., Hatlebakk, J. G., Hausken, T. 2014. Interaction between ingested nutrients and gut endocrine cells in patients with irritable bowel syndrome. Int. J. Mol. Med. 34:363-371.
Evers, A. D., Bechtel, D. B. 1988. Microscopic structure of the wheat grain. In: Pomeranz, Y. (ed.), Wheat chemistry and technology, Vol. 1. AACC International, Inc., St. Paul, MN. pp. 47-92.
Fasano, A., Berti, I., Gerarduzzi, T., Not, T., Colletti, R. B., Drago, S., Elitsur, Y., Green, P. H. R., Guandalini, S., Hill, I. D., Pietzak, M., Ventura, A., Thorpe, M., Kryszak, D., Fornaroli, F., Wasserman, S. S., Murray, J. A., Horvath, K. 2003. Prevalence of celiac disease in at-risk and not-at-risk groups in the United States: a large multicenter study. Arch. Intern. Med.163:286-292.
Fausch H., Kündig W., Neukom H. 1963. Ferulic acid as a component of a glycoprotein from wheat flour. Nature. 199:287-287.
Feeney, K. A., Wellner, N., Gilbert, S. M., Halford, N. G., Tatham, A. S., Shewry, P. R. 2003. Molecular structures and interactions of repetitive peptides based on wheat glutenin subunits depend on chain length. Biopolymers. 72:123-131.
Feiz, L., Martin, J. M., Giroux, M. J. 2008. Relationship between wheat (Triticum aestivum L.) grain hardness and wet-milling quality. AACC International, Inc. Cereal Chem. 85:44–50.
Field, M. J., Shewry, P. R., Miflin, B. J. 1983. Solubilization and characterization of wheat gluten proteins: Correlations between the amount of aggregated proteins and baking quality. J. Sci. Food Agric.34, 370-377.
Fincher, G. B., Stone, B. A. 1974. Some chemical and morphological changes induced by gibberellic acid in embryo-free wheat grain. Aust. J. Plant. Physiol. 1: 297-311.
Fitzgerald, M. A., Martin, M., Ward, R. M., Park, W. D., Shead, H. J. 2003. Viscosity of rice flour: A rheological and biological study. J. Agric. Food Chem. 51:2295- 2299.
Fleckenstein, B., Qiao, S. W., Larsen, M. R., Jung, G., Roepstorff, P., Solid, L. M. 2004. Molecular characterization of covalent complexes between tissue transglutaminase and gliadin peptides. J. Biol. Chem. 279:17607-17616.
Flowers, M., Peterson, C. J., Burns, J., Kuehner, J. 2008. Norwest 553. Hard red winter wheat. EM 8967-E. New wheat varieties. Oregon State University Extension Service.
Flowers, M., Peterson, C. J. 2010. ORSS-1757. “SuperSoft” Soft white winter wheat. EM 9016. New wheat varieties. Oregon State University Extension Service. 116
Fraser, J. R., Holmes, D. C., 1959. Proximate analysis of wheat-flour carbohydrates. IV. Analysis of whole-meal flour and some of its fractions. J. Sci. Food and Agri. 10:506-12.
French, A. D., Murphy, V. G. 1977. Computer modelling in the study of starch. Cereal Food. World. 22: 61-70.
Gan, Z., Ellis, P. R., Schofield, J. D. 1995. Mini review: gas cell stabilization and gas retention in wheat bread dough. J. Cereal Sci. 21: 215-230.
Gautier, M. F., Aleman, M. E., Guirao, A., Marion, D., Joudrier, P. 1994. Triticum aestivum puroindolines, two basic cystine-rich seed proteins: cDNA sequence analysis and developmental gene expression. Plant Mol. Biol. 25:43-57.
Gianibelli, M. C., Larroque, O.R., MacRitchie, F. Wrigley C. W. 2001. Biochemical, genetic, and molecular characterization of wheat glutenin and its component subunits. Cereal Chem. 78:635-646.
Giroux, M. J., and Morris, C. F. 1998. Wheat grain hardness results from highly conserved mutations in the friabilin components puroindoline a and b. Proc. Natl. Acad. Sci. 95:6262-6266.
Goesaert, H., Brijs, C., Veraverbeke, W. S., Courtin, C. M., Gebruers, K., Delcour, J. A. 2005. Wheat constituents: how they impact bread quality, and how to impact their functionality. Trends Food Sci. Technol. 16:12-30.
Goesaert, H., Gebruers, K., Courtin C. M., Brijs, K., Decour, J. A. 2006. Enzymes in breadmaking. In: Hui, Y. H., Corke, H., De Leyn, I., Cross, N. (Eds.), Bakery products: science and technology. Blackwell Publishing, Ames, IA. pp. 337-364.
Gooding, M.J., 2009. The wheat crop. In: Khan, K., Shewry, P. R. (Eds.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 19-50.
Gruppen, H., Hamer, R. J., Voragen, A. G. J. 1992. Water-unextractable cell wall material from wheat flour. II. Fractionation of alkali-extractable polymers and comparison with water extractable arabinoxylans. J. Cereal Sci. 16:53-67.
Gruppen, H., Komelink, F. J. M., Voragen, A. G. J. 1993. Water-unextractable cell wall material from wheat flour. III. A structural model for arabinoxylans. J. Cereal Sci. 19:111-128.
Guerrieri, N., Alberti, E., Lavelli, V., Cerletti, P. 1996. Use of spectroscopic and fluorescence techniques to assess heat-induced molecular modifications of gluten. Cereal Chem. 73:368-74.
Gupta, R. B., Batey, I. L., MacRitchie, F. 1992. Relationships between protein 117
composition and functional properties of wheat flours. Cereal Chem. 69:125-131.
Gupta, R. B., Khan, K., Macritchie, F., 1993. Biochemical Basis of Flour Properties in Bread Wheats. I. Effects of Variation in the Quantity and Size Distribution of Polymeric Protein. J. Cereal Sci. 18:23-41.
Gupta, R. B., MacRitchie, F. 1994. Allelic variation at glutenin subunit and gliadin loci, Glu-1, Glu-3 and Gli-1 of common wheats. II. Biochemical basis of the allelic effects on dough properties. J. Cereal Sci. 19:19-29.
Hayta, M., Schofield, J. D. 2004. Heat and additive induced biochemical transitions in gluten from good and poor breadmaking quality wheats. J. Cereal Sci. 40:245-256.
Henry, R. J. 1985. A comparison of the non-starch carbohydrates in cereal grains. J. Sci. Food Agric. 36:1243-1253.
Hizukuri, S., Takeda, Y., Yasuda, M. 1981. Multi-branched nature of amylose and the action of debranching enzymes. Carbohyd. Res. 94:205-213.
Hoseney, R. C., Faubion, J. M. 1981. A mechanism for the oxidative gelation of wheat flour water-soluble pentosans. Cereal Chem. 58:421-423.
Hoseney, R. C. 1984. Functional properties of pentosans in baked foods. Food Technol., 38:114-117.
Hoseney, R. C., Zeleznak, K., Lai, C. S. 1986. Wheat gluten: a glassy polymer. Cereal Chem. 63:285-286.
Hsia, C. C., Anderson, O. D. 2001. Isolation and characterization of wheat ω-gliadin genes. Theor. Appl. Genet. 103:37-44.
Hu, Y., Liu, Z., Cen, T., Sun H. 2007. The research on the analysis method of the wheat flour’s particle size distribution by laser diffraction. Journal of Henan University of Technology (Natural Science Edition). 28:22-32.
Huang, D. Y., Khan, K. 1997. Characterization and quantification of native glutenin aggregates by multitasking sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) procedure. Cereal Chem. 74:229-234.
Ingelbrecht, J. A., Verwimp, T., Grobet, P. J., Delcour, J. A. 2001. Behavior of Triticum durum Desf. arabinoxylans and arabinogalactan peptides during industrial pasta processing. J. Agric. Food Chem. 49:1783-1789.
Izydorczyk, M. S., Biliaderis, C. G., Bushuk, W. 1990. Oxidative gelation studies of water-soluble pentosans from wheat. J. Cereal Sci., 11:153-169.
118
Izydorczyk, M. S., Biliaderis, C. G. 1995. Cereal arabinoxylans: advances in structure and physicochemical properties. Carbohydr. Polym. 28:33-48.
Izydorczyk, M. S., Dexter, J. E. 2008. Barley β-glucans and arabinoxylans: molecular structure, physicochemical properties, and uses in food products – a review. Food Res. Int. 41:850-868.
Jazaeri, S. 2013. Conformational change in the structure of wheat proteins during mixing in hard and soft wheat doughs. [DPhil dissertation]. Guelph, Ontario, Canada: The University of Guelph. 105 p.
Jeffers, H. C., Rubenthaler, G. L. 1977. Effect of roll temperature on flour yield with the Brabender Quadrumat experimental mills. Cereal Chem. 54:1018-1025.
Jelaca, S. L., Hlynca, I. 1971. Water binding capacity of wheat flour crude pentosans and their relation to mixing characteristics of dough. Cereal Chem., 48:211-222.
Jelaca, S. L., Hlynca, I. 1972. Effect of wheat-flour pentosans in dough, gluten and bread. Cereal Chem. 49:489-495.
Johansson, E., Prieto-Linde, M. L., Jonsson, J. O. 2001. Effects of wheat cultivar and nitrogen application on storage protein composition and breadmaking quality. Cereal Chem. 78:19-25.
Jolly, C. J., Rahman, S., Kortt, A. A., Higgins, T. J. V. 1993. Characterisation of the wheat Mr 15000 “grain-softness protein” and analysis of the relationship between its accumulation in the whole seed and grain softness. Theor. Appl. Genet. 86:589-597.
Jolly, C. J., Glenn, G. M., Rahman, S. 1996. GSP-1 genes are linked to the grain hardness locus (Ha) on wheat chromosome 5D. Proc. Nat. Acad. Sci. Usa. 93:2408-2013.
Jonnala, R. S., MacRitchie, F., Smail, V. W., Seabourn, B. W., Tilley, M., Lafiandra, D., Urbano, M. 2010. Protein and quality characterization of complete and partial near-isogenic lines of waxy wheat. Cereal Chem. 87:538-545.
Junker, Y., Zeissig, S., Kim, S. J., Barisani, D., Wieser, H., Leffler, D. A., Schuppan, D. 2012. Wheat amylase trypsin inhibitors drive intestinal inflammation via activation of toll-like receptor 4. J. Exp. Med. 209:2395-2408.
Kalichevsky, M. T., Ring, S. G. 1987. Incompatibility of amylose and amylopectin in aqueous solution. Carbohydr. Res. 162:323-328.
Karell, K., Louka, A. S., Moodie, S. J., Ascher, H., Clot, F., Greco, L., Ciclitira, P. J., Sollid, L. M., Partanen, J. 2003. HLA types in celiac disease patients not carrying 119
the DQA1*05-DQB1*02 (DQ2) heterodimer: results from the European Genetics Cluster on Celiac Disease. Hum. Immunol. 64:469-477.
Kasarda, D. D., Adalsteins, A. E., Laird, N. F. 1987. Gamma-gliadins with alpha-type structure coded on chromosome 6B of the wheat (Triticum aestivum L.) cultivar “Chinese Spring.” In: Budapest, H., Lásztity R., Békés F. (Eds.), Proceedings of the 3rd International Workshop on Gluten Proteins. World Scientific: Singapore. pp. 20-29.
Kasarda, D. D. 1999. Glutenin polymers: The in vitro to in vivo transition. Cereal Food. World. 44:566-571.
Kasarda, D. D. 2013. Can an increase in celiac disease be attributed to an increase in the gluten content of wheat as a consequence of wheat breeding? J. Agric. Food Chem. 61:1155-1159.
Kawamura Y., Yonezawa, D. 1982. Wheat flour proteinases and their action on gluten proteins in diluted acetic acid. Agric. Biol. Chem. 46:767-773.
Kephart, K. D. 1991. Commercial Wheat Varieties of the United States. Wheat Industry Resource Committee.
Khamsi, R. 2014. “ Gluten sensitivity” may be a misnomer for distinct illness to various wheat proteins. Sci. Am. 310:2.
Kimber, G., Sears, E. R., 1987. Evolution in the genus Triticum and the origin of cultivated wheat. In: Heyne, E. G. (Ed.), Wheat and wheat improvement. ASA, Madison, WI. pp. 154–164.
Kiszonas, A. M., Patrick, F. E., Morris, C. F. 2013. Wheat arabinoxylan structure provides insight into function. Cereal Biomacromol. 90:387-195.
Koehler, P. 2010. Structure and functionality of gluten proteins: an overview. In: Branlard, G. (Ed), Gluten proteins. INRA, Paris, France. pp. 84-88.
Koehler, P., Wieser, H. 2013. Chemistry of cereal grains. In: Gobbetti, M., Ganzle, M. (Eds.), Handbook on sourdough biotechnology. Springer Science+Business Media, New York, pp. 11-39.
Kovacs, M. I. P., Fu, B. X., Woods, S. M., Khan, K. 2004. Thermal stability of wheat gluten protein its effect on dough properties and noodle texture. J. Cereal Sci. 39:9-19.
Kruger, J. E., Reed, G. 1988. Enzymes and color. In: Pomeranz, Y. (Ed.), Wheat chemistry and technology, 3rd ed., Vol. 1. AACC International, Inc., St. Paul, MN. pp. 441-500. 120
Kuktaite, R., Johansson, E., Juodeikiene, G., 2000. Composition and concentration of proteins in Lithuanian wheat cultivars: relationships with bread-making quality. Cereal Res. Communications 28:195-202.
Kuktaitea, .R, Larssonb, H., Johansson, E. 2004. Variation in protein composition of wheat flour and its relationship to dough mixing behavior. J. Cereal Sci. 40:31-39.
Labat, E., Morel, M. H., Rouau, X. 2000. Effects of laccase and ferulic acid on wheat flour doughs. Cereal Chem. 77:823-828.
Lagrain, B., Brijs, K., Veraverbeke, W. S., Delcour, J. A. 2005. The impact of heating and cooling on the physico-chemical properties of wheat gluten-water suspensions. J. Cereal Sci. 42:327-333.
Lagrain, B., Thewissen, B. G., Brijs, K., Delcour ,J. A. 2008. Mechanism of gliadin- glutenin cross-linking during hydrothermal treatment. Food Chem. 107:753-760.
Law, C. N., Worland, A. J., Giorgi, B., 1976. Genetic-Control of Ear-Emergence Time by Chromosomes-5a and Chromosomes-5d of Wheat. Heredity. 36:49-58.
Lefebvre, J., Popineau, Y., Deshayes, G., Lavenant, L. 2000. Temperature-induced changes in the dynamic rheological behavior and size distribution of polymeric proteins for glutens from wheat near-isogenic lines differing in HMW glutenin subunit composition. Cereal Chem. 77:193-201.
Levine, H., Slade, L. 1990. Influences of the glassy and rubbery states on the thermal, mechanical and structural properties of doughs and baked products. In: Faubion J. M., Faridi, H. (Eds.), Dough rheology and baked product texture, New York: Van Nostrand Reinhold. pp. 157-330.
Li, W., Cui, S. W., Kakuda, Y., 2006. Extraction, fractionation, structural and physical characterization of wheat beta-D-glucans. Carbohydr. Polym. 63:408-416.
Lineback, D. R., Rasper, V. F., 1988. Wheat carbohydrates. In: Pomeranz, Y. (Ed.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 277-372.
Lookhart, G. L., Bean, S. R. 1996. High-performance capillary electrophoresis (HPCE): An overview of a new method to characterize gluten proteins. In: Wrigley, C. W. (Ed.), RACI: Melbourne, Australia. pp. 399-402.
Loosveld, A., Maes, C., van Casteren, W. H. M., Schols, H. A., Grobet, P. J., Delcour, J. A. 1998. Structural variation and levels of water-extractable arabinogalactan- peptide in European wheat flours. Cereal Chem. 75:815-819.
121
Loosveld, A. M. A., Delcour, J. A., 2000. The significance of arabinogalactan-peptide for wheat flour bread-making. J. Cereal Sci. 32:147-157.
MacMurray, T. A, Morrison, W. R. 1970. Composition of wheat-flour lipids. J. Sci. Food Agric. 21:520-528.
MacRitchie, F., Lafiandra, D., 1997. Structure-function relationships of wheat proteins. In: Damodaran, S., Paraf, A. (Eds.), Food proteins and their applications. Marcel Dekker, Inc., New York. pp. 293-324.
MacRitchie, F. 1999. Wheat proteins: characterization and role in flour functionality. Cereal Food. World. 44:188-193.
Maningat, C. C., Seib, P. A., Bassi, S. D. and Woo, K. S. 2009. Wheat Starch: production, properties, modification, and uses. In: BeMiller, J., Whistler, R. (Eds.), Starch: chemistry and technology. Academic Press, New York. pp. 441-510.
Mansour, K. 1993. Sprout damage in wheat and its effect on wheat flour products. In: Walkin-Simmons M. K., Ried, J. L. (Eds.), Pre-harvest sprouting in cereals. AACC International, Inc., St. Paul, MN, pp. 8-9.
Marchylo, B. A., Kruger, J. E., Hatcher, D. W. 1989. Quantitative reversed-phase high- performance liquid chromatographic analysis of wheat storage proteins as a potential quality prediction tool. J. Cereal Sci. 9:113-130.
Mares, D. J., Stone, B. A. 1973. Studies on wheat endosperm. I. Chemical composition and ultrastructure of the cell walls. Aust. J. Biol. Sci. 26:793-812.
Masci, S., Lew, E. J. L., Lafiandra, D., Porceddu, E., and Kasarda, D. D. 1995. Characterization of low molecular weight glutenin subunits in durum wheat by reversed-phase high-performance liquid chromatography and N-terminal sequencing. Cereal Chem. 72:100-104.
Matsoukas, N. P., Morrison, W. R. 1991. Breadmaking quality of 10 Greek bread wheats: 2. Relationships of protein, lipid and starch components to baking quality. J. Sci. Food Agric. 55:87-101.
McFall, K. L., Fowler, M. E. 2009. Overview of wheat classification and trade. In: Carver, B.F. (Ed.), Wheat Science and Trade. Wiley-Blackwell, Ames, Iowa, pp. 439-454.
McIntosh, R. A., Hart, G. E., Devos, K. M., Gale, M. D., Rogers, W. J. 1998. Catalogue of gene symbols for wheat Proceedings of the Ninth International Wheat Genetics Symposium. University of Saskatchewan Extension Press, Canada. 122
Mejias, J. H., Lu, X., Osorio, C., Ullman, J. L., von Wettstein, D., Rustgi, S. 2014. Analysis of wheat prolamins, the causative agents of celiac sprue, using reversed phase high performance liquid chromatography (RP-HPLC) and matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). Nutrients. 6:1578-1597.
Meresse, B., Ripoche, J., Heyman, M., Cerf-Bensussan, N. 2009. Celiac disease: from oral tolerance to intestinal inflammation, autoimmunity and lymphomagenesis. Mucosal Immuol. 2:8-23.
Metakovsky, E. V., Branlard, G. P., Graybosch, R. A. 2006. Gliadins of common wheat: polymorphism and genetics. In: Wrigley, C., Bekes, F., Bushuk W. (Eds.), Gliadin and glutenin: the unique balance of wheat quality. AACC International, Inc., St. Paul, MN. pp. 35-84.
Metzger, R. J., Sibaugh, B. A. 1970. Location of genes for seed coat color in hexaploid wheat, Triticum aestivum L. Crop Sci. 10:495-496.
Meuser, F., Suckow, P. 1986. Non-starch polysaccharides. In: Blanshard J. M. V., Frazier P. J., Galliard T. (Eds), Chemistry and physics of baking. The Royal Society of Chemistry, London. pp. 42-61.
Mikulikova, D. 2007. The effect of friabilin on wheat grain hardness. Czech J. Genet. Plant Breed. 43:35-43.
Miles, M. J., Morris, V. J., Orford, P. D., Ring, S. G. 1985. The roles of amylose and amylopectin in the gelation and retrogradation of starch. Carbohydr. Res. 135:271-281.
Miskelly, D., Batey, I. L., Suter, D. A. I. 2010. Processing wheat to optimize product quality. In: Wrigley, C. W., Batey, I. L. (Eds.), Cereal grains: accessing and managing quality. CRC Press, Boca Raton, FL. pp. 431-457.
Mitea, C., Salentijn, E. M. J., van Veelen, P., Goryunova, S. V., van der Meer, I. M., van den Broeck, H. C., Mujico, J. R., Monserrat, V., Gilissen, L. J. W. J., Drijfhout, J. W., Dekking, L., Koning, F., Smulders, M. J. M. 2010. A universal approach to eliminate antigenic properties of alpha-gliadin peptides in celiac disease. PLoS One. 5.
Morris, C. F., Greenblatt, G. A., Bettge, A. D., Malkawi, H. I., 1994. Isolation and characterization of multiple forms of friabilin. J. Cereal Sci. 20:167-174.
Morris, C. F., Rose, S. P. 1996. Wheat. In: Henry, R. J., Kettlewell, P. S. (Eds.), Cereal grain quality. Chapman and Hall: London. pp. 3-54.
123
Morris, C. F. 2002. Puroindolines: the molecular genetic basis of wheat grain hardness. Plant Mol. Biol. 48:633-647.
Morrison, W. R., Mann, D. L., Soon, W., Coventry, A. M. 1975. Selective extraction and quantitative analysis of non-starch and starch lipids from wheat flour. J. Sci. Food Agric. 26:507-521.
Morrison, W. R. 1978. Wheat lipid composition. Cereal Chem. 55:548-558.
Morrison, W. R., Law, R. V., Snape, C. E. 1993. Evidence for inclusion complexes of lipids with V-amylose in maize, rice and oat starches. J. Cereal Sci. 18:107-109.
Mowat, A. M. 2003. Coeliac disease – a meeting point for genetics, immunology, and protein chemistry. Rapid Rev. 361:1290-1292.
Mustalahti, K., Catassi, C., Reunanen, A., Fabiani, E., Heier, M., McMillan, S., Murray, L., Metzger, M. H., Gasparin, M., Bravi, E. 2010. The prevalence of celiac disease in europe: results of a centralized, international mass screening project. Ann. Med. 42:587-595.
NASS. 2012. "National Agricultural Statistics Service." United States Department of Agriculture.
Nicolas, J., Billaud, C., Rouet-Mayer, M. A., Philippon, J. 2003. Enzymatic browing. 1. Biochemical aspects. In: Caballero, B., Trugo, L., Finglas, P. M. (Eds.), Encyclopaedia of food science, food technology and nutrition, 2nd ed., Vol. 1. Academic Press, London. pp. 678-686.
Noel, T. R., Parker, R., Ring, S. G., Tatham, A. S. 1995. The glass-transition behaviour of wheat gluten proteins. Int. J. biol Macromol. 17:81-85.
Ohm, J. B., Ross, A. S., Peterson, C. J., Ong, Y.-L. 2008. Relationships of high molecular weight glutenin subunit composition and molecular weight distribution of wheat flour protein with water absorption and color characteristics of noodle dough. Cereal Chem. 85:123-131.
Osborne, T. B. 1907. The proteins of the wheat kernel Carnegie Institution of Washington, Washington, D.C.
Osborne, B. G., Fearn, T. 1983. Collaborative evaluation of near-infrade reflectance analysis for the determination of protein, moisture and hardness in wheat. Journal of the Science of Food and Agriculture 34:1011-1017.
Pareyt, B., Finnie, S., Putseys, J., Delcour, J. 2011. Lipids in bread making: sources, interactions, and impact on bread quality. J. Cereal Sci. 54:266-279. 124
Payne, P. I., Corfield, K. G. 1979. Subunit composition of wheat glutenin proteins, isolated by gel filtration in a dissociating medium. Planta. 145:83-88.
Payne, P. I., Holt, L. M., Jackson, E. A., Law, C. N. 1984. Wheat storage proteins: their genetics and their potential for manipulation by plant breeding. Philos. Trans. R. Soc. Lond. B. 304:359-371.
Payne, P. I. 1987. Genetics of wheat storage proteins and effect of allelic variation on bread making quality. Annu. Rev. Plant Physiol. 38:141-153.
Payne, P. I., Nightingale, M. A., Krattiger, A. F., Holt, L. M. 1987. The relationship between HMW glutenin subunit composition and the bread-making quality of British-grown wheat varieties. J. Sci. Food Agric. 40:51-65.
Pérez, S., Baldwin, P. M., Gallant, D. J. 2009. Structural features of starch granules I. In: BeMiller, J., Whistler, R. (Eds.), Starch: chemistry and technology. Academic Press, New York. pp. 149-192.
Perlin, A. S. 1951. Isolation and composition of the soluble pentosans of wheat flour. Cereal Chem. 28:370-381.
Perten, H. 1964. Application of the falling number method for evaluating alpha-amylase activity. Cereal Chem. 41:127-140.
Posner, E. S., Hibbs, A. N., 2005. Wheat: the raw material. In: Wheat flour milling. 2nd ed. AACC International, Inc., St. Paul, MN. pp. 1-46.
Posner, E. S. 2009. Wheat flour milling. In: Khan, K., Shewry, P. R. (Eds.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 19-50.
Ram, M. 2014. "Breeding Methods," Asoke K. Ghosh, PHI Learning Private Limited, Delhi.
Ramseyer, D. D., Bettge, A. D., Morris, C. F. 2011. Endogenous and enhanced oxidative cross-linking in wheat flour mill streams. Cereal Chem. 88:217-222.
Rawle, A. 1993. Basic principles of particle size analysis. Malvern instrument technical paper MRK034. Malvern Instruments Limited, Enigma Business Park, Grovewood Road, Malvern, Worcestershire, WR14 1XZ, UK.
Robbinson, D. S. 1991. Peroxidases and catalases in foods. In: Robbinson, D. S., Eskin, N. A. M. (Eds.), Oxidative enzymes in foods. Elsevier Appl. Sci. Chem. London. pp. 1-47.
125
Rombouts, I., Lagrain, B., Brijs, K., Delcour, J. A. 2010. β-Elimination reactions and formation of covalent cross-links in gliadin during heating at alkaline pH. J. Cereal Sci. 52:362-67.
Ross, A. S., Bettge, A. D. 2009. Passing the test on wheat end-use quality. In: Carver, B.F. (Ed.), Wheat science and trade. Wiley-Blackwell, Ames, IA. pp. 455-486.
Ross, A., Flowers, M., Zemetra, R., Kongraksawech, T. 2012. Effect of grain protein concentration on falling number of ungerminated soft white winter wheat. Cereal Chem. 89:307-310.
Ross, A. S. 2013. FST 525 Bread week. Department of Food Science and Technology. Oregon State University. Corvallis, OR.
Roseborough, C. 2014. A historical evaluation of high molecular weight glutenins and the dwarfing (Rht) gene in Triticum aestivum in selected genotypes grown in the United States. [MSc dissertation]. OR, USA: Oregon State Univ. 111 p. Available from: ScholarsArchive online.
Rubio-Tapia, A., Kyle, R. A., Kaplan, E. L., Johnson, D. R., Page, W., Erdtmann, F., Brantner, T. L., Kim, W., Phelps, T. K., Lahr, B. D. 2009. Increased prevalence and mortality in undiagnosed celiac disease. Gastroenterol. 137:88-93.
Sapone, A., Bai, J. C., Ciacci, C., Dolinsek, J., Green, P. H. R., Hadjivassiliou, M., Kaukinen, K., Rostami, K., Sanders, D. S., Schumann, M., Ullrich, R., Villalta, D., Volta, U., Catassi, C., Fasano, A. 2012. Specturm of gluten-related disorders: consensus on new nomenclature and classification. BMC Medicine. 10:13-25.
Saulnier, L., Sado, P. E., Branlard, G., Charmet, G., Guillon, F. 2007. Wheat arabinoxylans: exploiting variation in amount and composition to develop enhanced varieties. J. Cereal Sci. 46:261-281.
Schofield, J. D., Bottomley, R. C., Timms, M. F., Booth, M. R. 1983. The effect of heat on wheat gluten and the involvement of sulphydryl-disulphide interchange reactions. J. Cereal Sci. 1:241-253.
Shan, L., Molberg, O., Parrot, I., Hausch, F., Filiz, F., Gray, G. M., Sollid, L. M., Khosla, C. 2002. Structural basis for gluten intolerance in celiac sprue. Science. 297:2275- 2279.
Shelton, D. R., Lee, W. J., 2000. Cereal carbohydrates. In: Kulp, K., Ponte, J. G. (Eds.), Handbook of cereal science and technology. Marcel Dekker, Inc., New York. pp. 385-415.
126
Shewry. P. R., Halford N. G., Tatham A. S. 1992. High-molecular-weight subunits of wheat glutenin. J. Cereal. Sci. 15:105-120.
Shewry, P. R., Lucas, J. A. 1997. Plant proteins that confer resistance to pests and pathogens. Adv. Bot. Res. 26:135-192.
Shewry P. R., Popineau Y., Lafiandra D., Belton P. 2000. Wheat glutenin subunits and dough elasticity: findings of the eurowheat project. Trends Food Sci. Technol. 11:433-41.
Shewry, P. R. 2009. Wheat. J. Experimental. Botany. 60:1537-1553.
Shewry, P. R., D’Ovidio, R., Lafiandra, D., Jenkins, J. A., Mills, E.N.C., Bekes, F. 2009. Wheat grain proteins. In: Khan, K., Shewry, P. R. (Eds.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 223-298.
Shibanuma, K., Takeda, Y., Hizukuri, S., Shibata, S. 1994. Molecular structures of some wheat starches. Carbohydr. Polym. 25:111-116.
Singh, N. K., Shepherd, K. W. 1988. Linkage mapping of the genes controlling endosperm proteins in wheat. 1. Genes on the short arms of group 1 chromosomes. Theor. Appl. Genet. 66:628-641.
Singh, H., MacRitchie, F. 2004. Changes in proteins induced by heating gluten dispersions at high temperature. J. Cereal Sci. 39:297-301.
Singh, S., Singh, N., MacRitchie, F. 2011. Relationship of polymeric proteins with pasting, gel dynamic- and dough empirical-rheology in different Indian wheat varieties. Food Hydrocolloid. 25:19-24.
Slade, L., Levine, H., Finley, J. W. 1989. Protein-water interactions: water as a plasticizer of gluten and other polymers. In: Philips R. D., Finley J. W. (Eds.), Protein quality and the effects of processing. Marcel Dekker, Inc., New York. pp. 9-124.
Southan, M., MacRitchie, F., 1999. Molecular weight distribution of wheat proteins. Cereal Chem. 76:827-836.
Stallknecht, G. F., Gilbertson, K. M., Ranney, J. E. 1996. Alternative wheat cereals as food grains: Einkorn, emmer, spelt, kamut, and triticale. In: Janick J. (ed.), Progress in new crops. ASHS Press, Alexandria, VA. pp. 156-170.
Stoddard, F. L. 2004. Starch: chemistry. In: Wrigley, C. W., Corke, H., Walker, C. E. (Eds.), Encyclopedia of Grain Science, Vol. 3. Elsevier Academic Press: Amsterdam; Boston, MA. pp. 212-219.
127
Stone, B., Morell, M. K., 2009. Carbohydrates. In: Khan, K., Shewry, P. R. (Eds.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 299-362.
Strahm, A., Amado, R., Neukom, H. 1981. Hydroproline-galactoside as a protein- polysaccharide linkage in a water-soluble arabinogalactan-peptide from wheat endosperm. Phytochemistry. 20:1061-1063.
Sutton, K. H., Bietz, J. A. 1997. Variation among high molecular weight subunits of glutenin detected by capillary electrophoresis. J. Cereal Sci. 25:9-16.
Tao, H. P., Kasarda, D. D. 1989. Two-dimensional gel mapping and N-terminal sequencing of LMW-glutenin subunits. J. Exp. Bot. 40:1015-1020.
Tatham, A. S., Miflin, B. J., Shewry, P. R. 1985. The beta-turn conformation in wheat gluten proteins: relationship to gluten elasticity. Cereal Chem. 62:405-412.
Tatham, A. S., Masson P., Popineau Y. 1990. Conformational studies of peptides derived by the enzymatic-hydrolysis of a gamma-type gliadin. J. Cereal Sci. 11:1-13.
Tester, R. F. and Morrison, W. R. 1990. Swelling and gelatinization of cereal starches. I. Effects of amylopectin, amylose, and lipids. Cereal Chem. 67:551-557.
Tester, R. F., Debon, S. J. J. 2000. Annealing of starch – a review. Int. J. Biol. Macromol. 27:1-12.
Tonutti, E., Bizzaro, N. 2014. Diagnosis and classification of celiac disease and gluten sensitivity. Autoimmun. Rev. 13:472-476.
Treviranus, I. 2011. Interpreting your laser diffraction particle size analysis results (Decoding the acronyms and finding insights). HORIBA Particle slide share. Access online.
USDA. 2013. Wheat Data: Yearbook Tables.
U.S. Food and drug administration. 2014. Code of Federal Regulations Title 21. CFR137.105. Available online only. U.S. Department of health & human service, Washington, D. C.
Uthayakumaran, S., Listiohadi, Y., Baratta, M., Batey, I. L., Wrigley, C. W. 2006. Rapid identification and quantitation of high-molecular-weight glutenin subunits. J. Cereal Sci. 44:34-39. van den Broeck, H. C., van Herpen, T. W. J. M., Schuit, C., Salentijn, E. M. J., Dekking, L., Bosch, D., Hamer, R. J., Smulders, M. J. M., Gilissen, L. J. W. J., van der Meer, I. M. 2009. Removing celiac disease-related gluten proteins from bread 128
wheat while retaining technological properties: a study with Chinese Spring deletion lines. BMC Plant Biol. 9:41. van den Broeck, H. C., de Jong, H. C., Salentijn, E. M. J., Dekking, L., Bosch, D., Hamer, R. J., Gilissen, L. J. W. J., van der Meer, I. M., Smulders, M. J. M. 2010. Presence of celiac disease epitopes in modern and old hexaploid wheat varieties: wheat breeding may have contributed to increased prevalence of celiac disease. Theor. Appl. Genet. 121:1527-1539. van den Broeck, H. C., Smulders, M. J. M., Hamer, R. J., Gilissen, L. J. W. J., van der Meer, I. M. 2011. Coeliac-safe wheat. A novel wheat to decrease the prevalence and symptoms of coeliac disease. Food Saf. 22:18-21. van den Bulck, K., Loosveld, A. M. A., Courtin, C. M., Proost, P., Van Damme, J., Robben, J., Mort, A., Delcour, J. A. 2002. Amino acid sequence of wheat flour arabinogalactan-peptide, identical to part of grain softness protein GSP-1, leads to improved structural model. Cereal Chem. 79:329-331. van Hung P., Maeda T., Morita N. 2006. Waxy and high-amylose wheat starches and flours- characteristics, functionality and application. Trends Food Sci. Technol. 17:448-456.
Veraverbeke W. S., Delcour, J. A. 2002. Wheat protein composition and properties of wheat glutenin in relation to breadmaking functionality. Cr. Rev. Food Sci. 42:179-208.
Verdu, E. F., Armstrong, D., Murray, J. A. 2009. Between celiac disease and irritable bowel syndrome: the “no man’s land” of gluten sensitivity. Am. J. Gasteroenterol. 104:1587-1594.
Verjans, P., Dornez, E., Delcour, J. A., and Courtin, C. M. 2010. Selectivity for water unextractable arabinoxylan and inhibition sensitivity govern the strong bread improving potential of an acidophilic GH11 Aureo-basidium pullulans xylanase. Food Chem. 123:331-337.
Vinkx, C. J. A., van Nieuwenhove, C. G., Delcour, J. A. 1991. Physicochemical and functional properties of rye non-starch polysaccharides. III. Oxidative gelation of a fraction containing water-soluble pentosans and proteins. Cereal Chem. 68:617- 622.
Vogel, O. A., R. E. Allan, and C. J. Peterson. 1963. Plant and performance characteristics of semidwarf winter wheats producing most efficiently in eastern Washington. Agron. J. 55:397-398.
129
Waigh, T. A., Gidley, M. J., Komanshek, B. U., Donald, A. M. 2000. The phase transformations in starch during gelatinisation: a liquid crystalline approach. Carbohydr. Res. 328:165-176.
Weegels, P. L., Degroot, A. M. G., Verhoek, J. A., Hamer R. J. 1994. Effects on gluten of heating at different moisture contents. 2. Changes in physicochemical properties and secondary structure. J. Cereal Sci. 19:39-47.
Weegels, P. L., van de Pijpekamp, A. M., Graveland, A., Hamer, R. J., Schofield, J. D. 1996. Depolymerization and re-polymerization of wheat glutenin during dough processing. I. Relationships between glutenin macropolymer content and quality parameters. J. Cereal Sci. 23:103-111.
Weisstein, E. W. 2015. "Cubic Spline." From MathWorld--A Wolfram Web Resource.
Whitaker, J. R. 1985. Mechanisms of oxidoreductases important in food modification. In: Richardson, T., Finley, J. W. (Eds.), Chemical changes in food during processing. Avi Publishing Co., Westport, CO. pp. 121-176.
Wieser, H. 1998. Investigations on the extractability of gluten proteins from wheat bread in comparison with flour. Z Lebensm Unters Forsch A. 207:128-132.
Wieser, H., Antes, S., Seilmeier, W. 1998. Quantitative determination of gluten protein types in wheat flour by reversed-phase high-performance liquid chromatography. Cereal Chem. 75:644-650.
Wieser H., Bushuk W., Macritchie, F. 2006. The polymeric glutenins. In: Wrigley, C., Bekes, F., Bushuk W. (Eds.), Gliadin and Glutenin: the unique balance of wheat quality. AACC International, Inc., St. Paul, MN. pp. 213-240.
Wieser H. 2007. Chemistry of gluten proteins. Food Microbiol. 24:15-19.
Wieser, H., Koehler, P. 2008. The biochemical basis of celiac disease. Cereal Chem. 85:1-13.
Wieser, H. Konitzer, K., Koehler, P. 2012. Celiac disease – multidisciplinary approaches. Cereal Food. World. 57:215-224.
Williams, P. C. 2000. Applications of the Perten SKCS 4100 in flour-milling. Association of Operative Millers Bulletin, March. pp. 7421-7524.
Wood, P. J. 2007. Cereal β-glucans in diet and health. J. Cereal Sci. 46:230-238.
Wrigley, C. W. 2009. Wheat: a unique grain for the world. In: Khan, K., Shewry, P. R. (Eds.), Wheat: chemistry and technology. AACC International, Inc., St. Paul, MN. pp. 1-15. 130
Wrigley, C., Asenstorfer, R., Batey, I., Cornish, G., Day, L., Mares, D., Mrva, K. 2009. The biochemical and molecular basis of wheat quality. In: Carver, B. F. (Ed.), Wheat science and trade. Wiley-Blackwell, Ames, IA. pp. 495-520.
Yamamori, M., Nakamura, T., Endo, T. R., Nagamine, T. 1994. Waxy protein- deficiency and chromosomal location of coding genes in common wheat. Theor. Appl. Genet. 89:179-184.
Zeeman, S. C., Kossmann, J., Smith, A. M. 2010. Starch: its metabolism, evolution, and biotechnological modification in plants. Annu. Rev. Plant Biol. 61:209-234.
Zhang, Y., He, Z. H., Ye, G. Y., Aimin, Z., and Van Ginkel, M. 2004. Effect of environment and genotype on bread-making quality of spring-sown spring wheat cultivars in China. Euphytica. 139:75-83.
Zhang, Y., He, Z. H., and Ye, G. Y. 2005. Milling quality and protein properties of autumn-sown Chinese wheats evaluated through multi-location trials. Euphytica. 143:209-222.
Zheng, H., Guo, B., Chen, X. L., Fan, S. J., and Zhang, Y. Z. 2011. Improvement of the quality of wheat bread by addition of glycoside hydrolase family 10 xylanases. App. Microbiol. Biotechnol. 90:509-515.
131
Appendices
Appendix 1: Samples for flour particle size determination (2011 OWEYT)
Three selected varieties Fifty randomly selected samples Location Sample Location Sample Location Sample Location Sample ID ID ID ID Bobtail Arlington 21.2 Lexington 18.2 Arlington 45.1 Lexington 45.1 Arlington 18.1 Lexington 14.3 Arlington 45.2 Lexington 45.2 Arlington 43.2 Lexington 36.2 Condon 45.1 Moro 45.1 Arlington 28.2 Lexington 32.2 Condon 45.2 Moro 45.2 Arlington 29.2 Lexington 20.1 Corvallis 45.1 N. Valley 45.2 Condon 38.2 Lexington 42.3 Corvallis 45.2 N. Valley 45.3 Condon 43.1 Moro 16.4 Hermiston 45.1 Pendleton 45.1 Condon 9.2 Moro 31.2 Hermiston 45.2 Pendleton 45.2 Condon 19.1 Moro 40.1 Coda Condon 31.2 Moro 10.1 Arlington 30.1 Moro 30.1 Corvallis 31.2 Moro 10.2 Arlington 30.2 Moro 30.2 Corvallis 19.2 Moro 7.2 Hermiston 30.1 N. Valley 30.1 Corvallis 10.1 Moro 13.2 Hermiston 30.2 N. Valley 30.2 Corvallis 36.2 Moro 6.2 Brundage 96 Corvallis 18.4 Moro 34.1 Arlington 8.1 Lexington 8.1 Corvallis 45.1 Moro 35.2 Arlington 8.2 Lexington 8.2 Hermiston 41.2 N. Valley 3.1 Condon 8.1 Moro 8.1 Hermiston 44.1 N. Valley 30.2 Condon 8.2 Moro 8.2 Hermiston 10.1 N. Valley 29.1 Corvallis 8.1 N. Valley 8.1 Hermiston 20.1 Pendleton 9.2 Corvallis 8.2 N. Valley 8.2 Hermiston 38.2 Pendleton 35.2 Hermiston 8.1 Pendleton 8.1 Hermiston 21.2 Pendleton 38.2 Hermiston 8.2 Hermiston 1.2 Pendleton 9.1 Hermiston 5.2 Pendleton 25.1 Lexington 9.1 Pendleton 6.1
132
Appendix 2: Samples applied in the historical study
Hard Wheat Soft Wheat Variety Year Variety Year Variety Year Variety Year 2137 2001 Neeley 1987 Alba 1959 Hymar 1944 Alton 1939 Newton 1984 Albit 1934 Hyslop 1974 Apache 1969 Overland 2011 Anderson 1959 Jones Fife 1919 Arapahoe 1993 Overlay 2006 Arthur 1974 Knox 1959 Blackhull 1924 Pawnee 1949 Arthur 71 1979 Lewjain 1988 Burt 1964 Ponca 1959 Baart 1919 Madsen 1919 Cache 1959 Probrand 812 1984 Blueboy 1974 Mediterranean 1991 Centura 1989 Ramona 44 1949 Brever 1954 Monon 1964 Centurk 1979 Rampart 2000 Bruehl 2003 Moro 1969 Centurk 78 1984 Redland 1991 Brundage 2005 Nugaines 1969 Chanute 1974 Redwin 1984 Butler 1954 Omar 1959 Cheyenne 1944 Rocky 1993 Caldwell 1984 ORCF 101 2009 Chiefkan 1944 Roughrider 1984 Clarkan 1949 Paha 1974 Chisholm 1990 Sage 1979 Coker 747 1984 Poole 1919 Comanche 1949 Scout 1969 Daws 1984 Red May 1919 Concho 1959 Scout 66 1979 Elgin 1949 Red Wave 1919 Duke 1979 Siouxland 1988 Elmar 1954 Rely 2000 Duster 2012 Sturdy 1974 Eltan 1994 Rex 1939 Endurance 2009 TAM 105 1984 Fairfield 1949 Seneca 1959 Fuller 2009 TAM 107 2002 Federation 1929 Sonora 1800 Genou 2007 TAM 111 2012 Forward 1939 Stephens 1984 Hard 1918 TAM W-101 1979 Fulcaster 1919 Thorne 1949 Federation Hatcher 2008 Tenmarq 1939 Fultz 1919 Triplet 1924 Improved 1964 Tiber 1992 Gaines 1964 Trumbull 1939 Triumph Jagalene 2005 Turkey 1919 Genesee 1959 Tubbs 2011 Jagger 1999 Vona 1984 Goldcoin 1919 Tubbs 06 2005 Kanred 1924 Westar 1949 Golden 1944 Vigo 1954 Kaw 1964 Weston 1991 Hart 1984 White Winter 1924 Harvest Kawvale 1939 Wichita 1954 1919 Wilhemina 1944 Queen Klassic 2002 Yellowstone 2009 Hill 81 1986 Yamhill 2008 Millennium 2004 Hybrid 128 1919 Yorkwin 1949